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What is Engineering Design and the Engineering Design Process?

Engineering design is a process. It involves tools and ways of thinking that people can use in almost any situation. Each part of the process reveals information about the problem and possible solutions.

Engineering design is iterative. Engineers are expected to repeat the process. Steps like defining, planning, modeling, and testing, can be completed in different sequences to find the best possible solution.

Engineering design is problem-solving. It always has a purpose that is specific and known. It may involve experiments to better understand the problem (or a possible solution), but the goal of engineering design is always to solve a problem.

Engineering design is finding the best solution. Evaluation is an important part of the process. Solutions have different strengths and weaknesses and have to stay within the physical limits of available time, cost, tools, and resources. Engineers have to choose the solution that provides the most desired features with the fewest negatives. That’s why engineering is often called “design under constraint”.

Engineering design is an iterative process used to identify problems and develop and improve solutions.

The engineering design process can be extremely useful to any individual trying to solve a problem. But engineering design can be – and usually is – done in teams. Each team member brings different knowledge and experience to the process, which usually improves the results.

Can anybody do this?

Yes! One great thing about engineering design is that it relies on skills that anyone can do and can learn to do better with practice.

Different models of the engineering design process include different “steps”, but there are a few skills that anyone doing engineering design is likely to use - and most of these you are already familiar with. Here's a list:

Ask questions
Do research
Draw/sketch
Construct models or prototypes
Conduct tests
Evaluate results
Present solutions

Professional engineers may work on problems that are bigger in scale and complexity, but the basic process for solving them is the same as the one you can use to solve everyday problems. The difference between planning a taco party and designing a city's water system is simply the amount of specialized knowledge and experience required to solve the problem.

Design and construct floating boom that will prevent an oil spill from reaching the beach and absorb as much oil as possible from a spill.

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What if my idea fails?

Failure is okay .

In engineering design, failure is expected. It helps you find the best solutions before implementing them in the “real world”. Having a prototype fail is a GOOD thing, because that means you have learned something new about the problem and potential solutions.

What does the engineering design process look like?

The engineering design process is a remarkably flexible and useful way to look at many different kinds of problems. Here's a fun video that uses a the engineering design process to successfully solve what seems like a non-engineering problem.

This example shows a number of different steps, but not everyone approaches the design process the same way. That's okay. There are actually several different models used to teach and talk about engineering design. Here are a couple of others that are pretty simple:

NGSS model of the engineering design process

Notice that neither of these models has a specific start or end point. Designers often jump around between steps.

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Engineering Design: An Overview of the Engineering Design Process and Best Practices

Table of Contents

Engineering Design

Introduction Engineering design is the process of creating solutions to problems through the application of scientific, mathematical, and engineering principles. Engineering design is a critical aspect of any successful project, whether it be a product, system, or infrastructure. The engineering design process is a systematic approach to problem-solving that involves a series of steps and best practices. This blog post will provide an overview of the engineering design process and best practices for designing products, systems, and infrastructure.

Engineering Design Domains

The Engineering Design Process is a series of steps that engineers undertake to elaborate a solution of a problem.

Product Design Product design is the general process of creation of new products and services.

Mechanical Design Mechanical engineering design is an engineering procedure aimed at designing parts, components, products, or systems of a mechanical nature while applying engineering physics and mathematics principles with materials science.

Electrical Design Electrical design is the process of planning, creating, testing or supervising the development and installation of electrical equipment, such as electrical components, schematics, lighting equipment, power systems, power distribution, fire and life safety systems, electronic components, voice and data telecommunications infrastructure.

Industrial Design Industrial design is the practice of designing products for mass production.

Civil Design Civil engineering design is a professional engineering discipline that deals with the design, dimensioning, and engineering of buildings and structural members, ensuring they to withstand different types of loads and forces applied to those members.

Architectural Design Architectural design is a concept that focuses on components or elements of a structure translating the non-physical design problem into the physical building product.

Software Design Software design is the practice of creating a specification of a software artifact, intended to accomplish goals, by means of planning a software solution including considerations of functional components, layers, algorithms, interfaces, integration, data, and security.

Popular Engineering Design Software

Here are some examples of popular software tools used in different areas of engineering:

Computer-Aided Design (CAD) Software: CAD software is used for creating 2D and 3D models of products, structures, and machines. Popular CAD software tools include AutoCAD, SolidWorks, CATIA, Pro/Engineer, and Inventor.
Finite Element Analysis (FEA) Software: FEA software is used to simulate and analyze the behavior of structures and materials under different loads and conditions. Popular FEA software tools include ANSYS, Abaqus, COMSOL, and Nastran.
Computational Fluid Dynamics (CFD) Software: CFD software is used to simulate and analyze the behavior of fluids and gases in different environments. Popular CFD software tools include ANSYS Fluent, STAR-CCM+, and OpenFOAM.
Electronic Design Automation (EDA) Software: EDA software is used for designing and simulating electronic circuits and systems. Popular EDA software tools include Cadence, Mentor Graphics, and Altium Designer.
Geographic Information System (GIS) Software: GIS software is used for creating and analyzing maps and geographic data. Popular GIS software tools include ArcGIS, QGIS, and Google Earth.
Project Management Software: Project management software is used for planning and tracking engineering projects. Popular project management software tools include Microsoft Project, Asana, Trello, and Jira.

The Engineering Design Process

The engineering design process is a systematic approach to problem-solving that involves a series of steps. The steps are as follows:

Define the Problem

The first step in the engineering design process is to define the problem. This involves understanding the problem, its scope, and the requirements of the project. The problem should be well-defined, measurable, and achievable.

Gather Information

The second step is to gather information. This involves researching the problem, collecting data, and understanding the constraints of the project. The information gathered should be used to develop a thorough understanding of the problem.

Generate Concepts

The third step is to generate concepts. This involves brainstorming, sketching, and exploring different ideas. The goal is to generate as many ideas as possible.

Evaluate Concepts

The fourth step is to evaluate the concepts. This involves selecting the most promising ideas and assessing their feasibility. The evaluation should take into account technical feasibility, economic viability, and market potential.

Develop a Solution

The fifth step is to develop a solution. This involves creating a detailed design that takes into account all the requirements of the project. The design should be technically sound, economically viable, and meet the needs of the market.

Test and Refine

The sixth step is to test and refine the solution. This involves building a prototype and testing it to ensure that it meets the requirements of the project. The prototype should be refined until it meets all the requirements.

Implement the Solution

The final step is to implement the solution. This involves producing the product, system, or infrastructure and bringing it to market. The implementation should be done in a way that ensures that the product, system, or infrastructure meets all the requirements of the project.

Best Practices for Designing Products

Understand the User : It is important to understand the user when designing products, systems, and infrastructure. This involves understanding their needs, wants, and limitations. The design should be created with the user in mind and should meet their needs.
Consider Sustainability : Sustainability should be considered when designing products, systems, and infrastructure. This involves designing products, systems, and infrastructure that are environmentally friendly and have a minimal impact on the environment.
Use High-Quality Materials: High-quality materials should be used when designing products, systems, and infrastructure. This ensures that the product, system, or infrastructure is durable and long-lasting.
Test and Validate: Testing and validating the design is critical to ensure that it meets the requirements of the project. This involves testing the design in a controlled environment and validating it against the requirements of the project.
Use Simulation and Modeling: Simulation and modeling can be used to test the design and ensure that it meets the requirements of the project. This involves creating a virtual model of the product, system, or infrastructure and simulating its performance.
Consider Safety and Reliability : Safety and reliability should be considered when designing products, systems, and infrastructure.
Optimize Cost and Performance: Cost and performance should be optimized when designing products, systems, and infrastructure. This involves finding the balance between the cost of the design and its performance.
Collaboration and Communication: Collaboration and communication are essential when designing products, systems, and infrastructure. This involves working with other members of the team to ensure that the design meets the requirements of the project and is feasible.
Use Design Standards and Regulations : Design standards and regulations should be followed when designing products, systems, and infrastructure. This ensures that the design meets the required safety, quality, and performance standards.
Continuous Improvement: Continuous improvement should be a part of the design process. This involves evaluating the design, gathering feedback, and making changes to improve its performance and meet the requirements of the project.
Examples of Engineering: Design Engineering design is used in a wide range of industries and applications. Here are some examples of engineering design in action:
Automotive Design: Automotive design involves the creation of vehicles that are safe, reliable, and efficient. The design process includes developing the body, chassis, engine, and transmission.
Aerospace Design : Aerospace design involves the creation of aircraft, spacecraft, and satellites. The design process includes developing the aerodynamics, propulsion, and control systems.
Civil Engineering Design : Civil engineering design involves the creation of infrastructure such as buildings, bridges, and roads. The design process includes developing structural, mechanical, and electrical systems.
Consumer Product Design : Consumer product design involves the creation of products such as electronics, appliances, and furniture. The design process includes developing the product’s aesthetics, ergonomics, and usability.
Environmental Engineering Design : Environmental engineering design involves the creation of systems and infrastructure that protect the environment. The design process includes developing waste management systems, water treatment systems, and renewable energy systems.

Conclusion Engineering design is a critical aspect of any successful project. The engineering design process provides a systematic approach to problem-solving, and best practices should be followed when designing products, systems, and infrastructure. Understanding the user, considering sustainability, using high-quality materials, testing and validating the design, using simulation and modeling, considering safety and reliability, optimizing cost and performance, collaborating and communicating, following design standards and regulations, and continuous improvement are all essential when designing products, systems, and infrastructure. Examples of engineering design can be seen in industries such as automotive design, aerospace design, civil engineering design, consumer product design, and environmental engineering design. By following the engineering design process and best practices, engineering design can create innovative and effective solutions to complex problems.

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What is engineering design?

Engineering design is the process of creating a solution to a problem or need using engineering principles and techniques. It involves the systematic application of scientific and mathematical principles to design a product, system, or infrastructure that meets specific requirements and satisfies the needs of the user.

What are the steps of the engineering design process?

The engineering design process typically involves the following steps: Define the problem Research and gather information Develop possible solutions Select the best solution Create a prototype or model Test and evaluate the prototype Refine and improve the design Finalize the design and prepare it for production

What are some best practices for engineering design?

Some best practices for engineering design include: Understanding the user’s needs and requirements Considering sustainability and the environmental impact of the design Using high-quality materials and components Testing and validating the design through simulations and prototypes Considering safety and reliability in the design Optimizing cost and performance Collaborating and communicating effectively with team members and stakeholders Following design standards and regulations Continuously improving and refining the design.

What are some examples of engineering design in action?

Engineering design can be seen in a variety of industries and applications, including: Automotive design Aerospace design Civil engineering design Consumer product design Environmental engineering design.

Why is engineering design important?

Engineering design is important because it allows engineers to create innovative and effective solutions to complex problems. By following a systematic design process and using best practices, engineers can create products, systems, and infrastructure that meet specific needs and requirements, while also being safe, reliable, and cost-effective. Effective engineering design can also have a positive impact on society and the environment.

Best Practice

3G PRINCIPLE 3K PRINCIPLE 3M PRINCIPLE 3R 4M PRINCIPLE Avoid These Common Mistakes on a Job Interview Benefits of Kaizen Business Analysis capacity management Continual improvement Cybersecurity Cycle Time daily work management Examples gemba gembutsu genjitsu Industrial Trainer KAIZEN Kaizen continuous improvement Kaizen principles Kanban Lead Time Lean meaning Muda Mura Muri Objectives philosophy Process Process Analysis Quality Circle R&D RECYCLE Reduce Research and Development REUSE Rockwell hardness Significance Six Sigma Supply Chain Optimization The Secret of Standardization The Toyota 3M Model Total Productive Maintenance What is Capacity Management?

The Engineering Design Process

The engineering design process is a series of steps that engineers use to create solutions to problems. It is a structured approach to problem-solving that involves identifying a problem, defining the problem, generating potential solutions, evaluating those solutions, selecting the best solution, and implementing and testing that solution. The engineering design process is iterative, meaning that engineers often repeat some or all of the steps several times to refine their solution. It is a critical component of the field of engineering, as it allows engineers to solve complex problems and create new products and technologies. Effective use of the engineering design process requires a strong understanding of engineering principles, scientific methods, and mathematical analysis.

The engineering design process involves making a series of decisions. Each decision must be carefully considered and justified based on various factors such as function, safety, cost, and feasibility. Throughout the process, engineers must weigh the benefits and drawbacks of different options and choose the best course of action based on their knowledge, experience, and the available resources. Justifying decisions is an essential part of the design process as it helps to ensure that the final product meets the required specifications and functions as intended. By justifying each decision, engineers can also communicate their thought process to others involved in the project, such as clients, stakeholders, or team members, and gain their trust and support.

The engineering design process is nearly always iterative in nature. Retracing steps and going back to a previous decision is a common occurrence in the engineering design process. As new information is gathered or unforeseen challenges arise, engineers may realize that an earlier decision was not the best one. In such cases, it is important to reevaluate the decision, consider alternative options, and make a new decision based on the updated information. This process of reiteration and adjustment is a fundamental part of the design process, as it allows engineers to continually improve and refine their designs until they meet the desired specifications. Accepting that retracing steps is sometimes necessary can be challenging, but it is essential to maintain a flexible mindset and be willing to adapt as new information comes to light. By doing so, engineers can ensure that they are producing the best possible outcome for their project.

While there is no one-size-fits-all approach to the engineering design process, a typical process often includes the following steps:

Define the problem : The first step in the design process is to clearly define the problem you are trying to solve. This involves understanding the needs and requirements of the user or customer, identifying any constraints or limitations, and setting specific goals and objectives.
Conduct research : Once the problem has been defined, engineers typically conduct research to gain a deeper understanding of the problem and potential solutions. This may involve reviewing existing products or technologies, conducting experiments, or gathering data through surveys or other methods.
Develop potential solutions : Using the information gathered during the research phase, engineers develop a range of potential solutions to the problem. This may involve brainstorming, sketching, or creating computer models of different designs.
Evaluate potential solutions : Engineers then evaluate each potential solution based on a set of criteria, such as cost, performance, safety, and ease of use. This may involve creating prototypes, conducting simulations, or performing experiments to test each design.
Select the best solution : After evaluating each potential solution, engineers select the best design to move forward with. This may involve combining different elements of different designs or modifying a design to better meet the requirements of the problem.
Implement and test the solution : Once a design has been selected, engineers implement and test the solution. This may involve building a prototype, performing additional simulations or experiments, or conducting user testing to ensure that the solution meets the needs of the user or customer.
Iterate and improve : The engineering design process is iterative, meaning that engineers often repeat some or all of these steps several times to refine their solution. This may involve making changes to the design based on user feedback or additional testing, or identifying new problems that need to be addressed.

Engineering Design Assumptions

Design Methodologies

Risk Mitigation through Design

Design Perspicuity

Engineering design – example projects.

Concurrent Design

FMEA - Failure Modes and Effects Analysis

Engineering design - example projects.

Engineering Design Explained: Process and Steps

April 19, 2024

The engineering design process is arguably the cornerstone of product development projects, be it small-scale customized products or hi-tech industrial equipment. It provides engineers with clear guidelines on how to plan projects and track their progress. In this article, we will discuss the engineering design cycle and process in detail.

What is Engineering Design?

Let us begin with an overview of what is engineering design. A simple definition would be the application of engineering techniques and principles to solve a design problem creatively and systematically.

As it may seem, even our simple definition is a bit of a handful. For this reason, designers divide the whole process into various engineering design process steps, which adds clarity and structure to their work.

The engineering design cycle starts at the ideation stage and sees the product through from prototype to production , at which point it is ready to enter the market.

Engineering Design Process

The upcoming sections dive deeper into each stage of the process.

Problem Definition

Problem definition is the first, and arguably the most important, among engineering design process steps. At this stage, the engineering team sits down with product stakeholders, which includes people like investors, production managers, and the sales team.

The requirements of the product design are discussed in detail and the expectations of the entire group are clearly understood. This includes high-level targets like product functionality, aesthetic requirements, budgetary constraints, material limitations, product quality, etc.

The problem definition provides a framework for designers to base their work on. Goals are set for subsequent steps in the engineering design cycle accordingly, and it is common to revisit the problem statement if issues arise.

In the research phase of the engineering design process, engineers carry out an in-depth study of the project. This phase involves a thorough investigation to understand the problem from various sources like academic literature reviews, design consultations, and engineering design principles.

This research gives designers specific insight into the problem and sets the stage for informed decision-making. At the end of this process, the designers are well aware of the problems they may encounter and the positives they can utilize.

Design Requirements

Once the research phase is complete, engineers can define design requirements by translating their insights into specific criteria. These criteria are technical, specific, and measurable.

In a common engineering design process example, these requirements include targets like product weight, volume, useful life, user safety, and manufacturing costs.

Feasibility Study

Next comes the feasibility phase. It is a critical phase in the engineering design process that focuses on evaluating the real-world practicality and viability of potential solutions. Engineers weigh their ideas against constraints coming from technical, environmental, financial, and legal aspects.

In doing so, designers develop a broader understanding of the project beyond the core design aspects. Moreover, they can identify potential risks, challenges, and technologies that can help them define a practical way forward.

What’s more, ensuring project feasibility is usually critical to securing funding from investors too.

Concept Generation

Feasible projects enter the concept generation phase. This is an intensive brainstorming stage where the design team pitches ideas for different design concepts. The focus is on creativity and problem-solving, where team members are encouraged to find all possible solutions to the problem at hand.

Intensive brainstorming generally leads to a pool of potential design concepts that the team can then move ahead with.

After this initial stage, however, design engineers have idea-specific discussions to sift out poor ideas and direct their focus on concepts worth exploring further. It is an important phase in the engineering design process as low potential ideas are filtered out and the team gains clarity and direction regarding their approach.

Initial Design

After finalizing a concept to go ahead with, designers now need to get their hands dirty. The initial design stage is all about creating rough outlines or initial drafts of the proposed solution, with a focus on key features and design requirements.

Moreover, such designs are also a good way to communicate your design intent and ideas to non-engineering teams. This is an important aspect of engineering design as cross-team communication is necessary for ensuring progress and transparency in the process.

Such initial designs serve as a starting point for further refinement in upcoming stages.

Optimize Design and Engineering Drawing

By the end of the initial design stage, designers have generally gone through a lengthy process of elimination to pinpoint the exact design they want to move forward with. However, at this point in the engineering design process, the initial design is still far from a functioning design.

Therefore, the detailed design and optimization stage bridges the gap between conceptual and functional design. First, the initial design will add requisite details. Dimensions are finalized, materials are chosen, tolerances are determined, and assembly setup/constraints are defined. These are hardcore mechanical design activities that ensure the functionality and safety of an engineering design.

Engineers use engineering drawings to consolidate and communicate all this information within and across teams. After design, an optimization study may also be performed. This is a good design and engineering practice. It aims to maximize the performance of the product by finding the best configuration of design inputs (dimensions, orientation, engineering fits, etc.).

Prototype Development and Testing

The prototype development and testing phase focuses on transforming the product design into a working, tangible form. In this stage of the engineering design process, various rapid prototyping methods like 3D printing or CNC machining are utilized to manufacture working prototypes of the design.

After production, engineers use rigorous testing to identify flaws and areas for improvement. Tests may include failure testing, fatigue testing, overloading, etc. All these tests aim to simulate the real working environment of the product and to verify whether the design can work efficiently in those conditions or not.

This phase is usually very iterative as each cycle of testing reveals new issues that require rework on the design. The movement from conceptual design to final design takes time and patience on the design team’s end.

Moreover, this phase in the engineering design cycle also includes an in-depth design for manufacturing study. This analysis focuses on fine-tuning the design to ensure compatibility with manufacturing processes. For example, sharp internal corners may be rounded for convenient CNC machining.

By the end of the time-consuming prototype development and testing phase, the design is completely ready to go into batch production.

The final phase in the engineering design process is production. After this stage, the team launches the product in the market and it gets into the hands of end-users. Therefore, it is a critical stage as mistakes are not affordable and the stakes are high.

In the context of engineering design, the design team forwards its work to the production department. The production team then prepares a manufacturing plan based on factors like deadlines, chosen manufacturing processes, DFM analysis, quality and quantity requirements, and material availability.

For example, if the product quantity is low, the production team focuses on low-volume production methods like CNC machining or vacuum casting rather than injection molding or die casting.

After manufacturing, the products pass through a quality check system where requirements like dimensional and form tolerances, surface quality, and heat treatments are verified.

Most companies work with a pre-defined quality threshold. For example, a company operating on 6-Sigma levels must make sure that there are less than 3.4 defects per million opportunities. Such requirements help maintain manufacturing quality.

Finally, the production team packages the products and delivers them to the supply chain department.

This point marks the end of the engineering design cycle.

Important Skills to Master for Engineering Design

Engineering design is a growing field with a lot of scope for young professionals. It is constantly evolving and absorbing related disciplines into its large body of knowledge, underpinning the fact that design engineers need to stay up to date with contemporary technology to stay competitive.

While the development of the engineering design cycle is a broad topic, we will highlight some of its core skills for design engineers.

Engineering Knowledge

The first and foremost requirement to become a talented design engineer is to acquire engineering knowledge. This includes having a strong grasp of topics like machine design, thermal and fluid systems, manufacturing, and CAD. This list can go on, but we highlight some of the main topics for a basic overview.

Of course, depending on the designer’s specific industry, the knowledge requirements can change. It is good practice to identify your interests early on and start developing yourself in those specific skills to get a head start.

Creative Problem Solving

Design is all about finding creative solutions to complex problems. A design engineer must have the creative ability to come up with novel solutions to engineering problems. Additionally, these solutions should also be in line with the set design requirements and must obey design standards.

As you may imagine, it can get tough to think of ideas that are not only new and creative but also practical from an engineering perspective. Therefore, aspiring design engineers should put themselves through design projects to polish their skills and become good at their job.

Communication and Teamwork

The engineering design process is not simple, as we have seen. One person can rarely handle all the workload alone. Therefore, it is common to work in a team of technical and non-technical people while working on an engineering design project.

Therefore, one of the most important skills to have as engineers and manufacturers is communication and teamwork. If you are a good collaborator, you can carry your team very well. Team members contribute their share of the workload, complement each other’s capabilities, and maintain respectfulness.

Similarly, communication is a key skill to possess in teamwork. There are numerous situations where ineffective communication can cause problems. For example, not being able to communicate quality requirements to the production department can have serious consequences in the form of failed products.

Engineering Design and Design for Manufacturing

In this last section, we would like to touch upon the topic of engineering design and design for manufacturing. Both are highly interlinked fields that work together to make a product successful.

The engineering design process was fully explained in the previous sections. As we saw, it has numerous engineering design process steps and clearly defined roles for design engineers at each step.

Design for manufacturing, in simple words, is a specialized field that focuses on making a design convenient to manufacture. This is done by altering the design in such a way that its functionality remains unaffected, but it gets cheaper and quicker to produce.

A common example of DFM analysis is the usage of filleted internal corners, removal of deep and narrow slots, and avoiding thin walls in CNC machining to ensure better surface quality and minimal tool wear. This does not change anything about product utility, but it drastically decreases part rejections, cycle times, and material wastage.

WayKen Supports Your Engineering Design - From Prototype to Production

Engineering designs optimize for manufacturing feasibility and efficiency, minimize potential production challenges, and reduce lead times and costs. As a professional rapid manufacturing company , WayKen enhances the engineering design process by offering advanced production and manufacturing solutions to meet specific needs. With WayKen’s support, you can identify and address potential production issues early on, ensuring smoother transitions from design to production.

The engineering design process is surely an outstanding methodology for working with design problems. The various engineering design process steps make the entire process very systematic and easy to follow, helping design teams organize their work in a better way.

Why is prototyping important in the engineering design cycle?

Prototyping is crucial for validating the engineering design before it goes into full-time production. Due to the complex nature of engineering design projects, mistakes are expected to occur. The prototyping stage sifts out such design flaws and allows designers to improve the design over iterations.

What tools are used for engineering design?

Engineering design utilizes numerous tools like CAD, CAE, FEM, machine learning, life cycle analysis, etc. The exact choice of engineering tools depends on the engineering design process step and the nature of the project.

How to find the right manufacturer for engineering design?

Some of the factors you should consider while choosing a manufacturer for engineering design include manufacturing capabilities, quality of service, cost, material availability, and customer service.

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An Inquiry-Based Introduction to Engineering pp 71–78 Cite as

Engineering Problem-Solving

Michelle Blum 2
First Online: 21 September 2022

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You are becoming an engineer to become a problem solver. That is why employers will hire you. Since problem-solving is an essential portion of the engineering profession, it is necessary to learn approaches that will lead to an acceptable resolution. In real-life, the problems engineers solve can vary from simple single solution problems to complex opened ended ones. Whether simple or complex, problem-solving involves knowledge, experience, and creativity. In college, you will learn prescribed processes you can follow to improve your problem-solving abilities. Also, you will be required to solve an immense amount of practice and homework problems to give you experience in problem-solving. This chapter introduces problem analysis, organization, and presentation in the context of the problems you will solve throughout your undergraduate education.

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Mark Thomas Holtzapple, W. Dan Reece (2000), Foundations of Engineering, McGraw-Hill, New York, New York, ISBN:978-0-07-029706-7.

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Aide, A.R., Jenison R.D., Mickelson, S.K., Northup, L.L., Engineering Fundamentals and Problem Solving, McGraw-Hill, New York, NY, ISBN: 978-0-07-338591-4.

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End of Chapter Problems

1.1 ibl questions.

IBL1: Using standard problem-solving technique, answer the following questions

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, draw the vector representation of your path (hint: use a compass legend to help create your coordinate system)

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate the velocity you ran in the north direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate the velocity you ran in the east direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate how far you ran in the north direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate how far you ran in the east direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, how far north have you traveled in 5 min?

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, how far east have you traveled in 5 min?

What type of problem did you solve?

IBL2: For the following scenarios, explain what type of problem it is that needs to be solved.

Scientists hypothesize that PFAS chemicals in lawn care products are leading to an increase in toxic algae blooms in lakes during summer weather.

An engineer notices that a manufacturing machine motor hums every time the fluorescent floor lights are turned on.

The U.N. warns that food production must be increased by 60% by 2050 to keep up with population growth demand.

Engineers are working to identify and create viable alternative energy sources to combat climate change.

1.2 Practice Problems

Make sure all problems are written up using appropriate problem-solving technique and presentation.

The principle of conservation of energy states that the sum of the kinetic energy and potential energy of the initial and final states of an object is the same. If an engineering student was riding in a 200 kg roller coaster car that started from rest at 10 m above the ground, what is the velocity of the car when it drops to 2.5 m above the ground?

Archimedes’ principle states that the total mass of a floating object equals the mass of the fluid displaced by the object. A 45 cm cylindrical buoy is floating vertically in the water. If the water density is 1.00 g/cm 3 and the buoy plastic has a density of 0.92 g/cm 3 determine the length of the buoy that is not submerged underwater.

A student throws their textbook off a bridge that is 30 ft high. How long would it take before the book hits the ground?

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Blum, M. (2022). Engineering Problem-Solving. In: An Inquiry-Based Introduction to Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-91471-4_6

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Solving Everyday Problems Using the Engineering Design Cycle

Hands-on Activity Solving Everyday Problems Using the Engineering Design Cycle

Grade Level: 7 (6-8)

(two 60-minutes class periods)

Additional materials are required if the optional design/build activity extension is conducted.

Group Size: 4

Activity Dependency: None

Subject Areas: Science and Technology

NGSS Performance Expectations:

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Engineering connection, learning objectives, materials list, worksheets and attachments, introduction/motivation, vocabulary/definitions, investigating questions, activity extensions, user comments & tips.

Engineers help design and create healthier tomorrows

This activity introduces students to the steps of the engineering design process. Engineers use the engineering design process when brainstorming solutions to real-life problems; they develop these solutions by testing and redesigning prototypes that work within given constraints. For example, biomedical engineers who design new pacemakers are challenged to create devices that help to control the heart while being small enough to enable patients to move around in their daily lives.

After this activity, students should be able to:

Explain the stages/steps of the engineering design process .
Identify the engineering design process steps in a case study of a design/build example solution.
Determine whether a design solution meets the project criteria and constraints.
Think of daily life situations/problems that could be improved.
Apply the engineering design process steps to develop their own innovations to real-life problems.
Apply the engineering design cycle steps to future engineering assignments.

Educational Standards Each TeachEngineering lesson or activity is correlated to one or more K-12 science, technology, engineering or math (STEM) educational standards. All 100,000+ K-12 STEM standards covered in TeachEngineering are collected, maintained and packaged by the Achievement Standards Network (ASN) , a project of D2L (www.achievementstandards.org). In the ASN, standards are hierarchically structured: first by source; e.g. , by state; within source by type; e.g. , science or mathematics; within type by subtype, then by grade, etc .

Ngss: next generation science standards - science, international technology and engineering educators association - technology.

View aligned curriculum

Do you agree with this alignment? Thanks for your feedback!

State Standards

Massachusetts - science.

Each group needs:

Marisol Case Study , one per student
Group Leader Discussion Sheet , one per group

To share with the entire class:

computer/projector setup to show the class the Introduction to the Engineering Design Cycle Presentation , a Microsoft® PowerPoint® file
paper and pencils
(optional) an assortment of scrap materials such as fabric, super glue, wood, paper, plastic, etc., provided by the teacher and/or contributed by students, to conduct the hands-on design/build extension activity

(Have the 19-slide Introduction to the Engineering Design Cycle Presentation , a PowerPoint® file, ready to show the class.)

Have you ever experienced a problem and wanted a solution to it? Maybe it was a broken backpack strap, a bookshelf that just kept falling over, or stuff spilling out of your closet? (Let students share some simple problems with the class). With a little bit of creativity and a good understanding of the engineering design process, you can find the solutions to many of these problems yourself!

But what is the engineering design process? (Listen to some student ideas shared with the class.) The engineering design process, or cycle, is a series of steps used by engineers to guide them as they solve problems.

(Show students the slide presentation. Refer to the notes under each slide for a suggested script and comments. The slides introduce the main steps of the engineering design process, and walk through a classroom problem—a teacher’s disorganized desk that is preventing timely return of graded papers—and how students devise a solution. It also describes the work of famous people—Katherine Johnson, Lee Anne Walters, Marc Edwards, James E. West and Jorge Odón—to illustrate successful examples of using the steps of the engineering design process.)

Now that we’ve explore the engineering design process, let’s see if we can solve a real-world problem. Marisol is a high-school student who is very excited to have their own locker. They have lots of books, assignments, papers and other items that they keep in their locker. However, Marisol is not very organized. Sometimes they are late to class because they need extra time to find things that were stuffed into their locker. What is Marisol’s problem? (Answer: Their locker is disorganized.) In your groups, you’ll read through Marisol’s situation and see how they use the engineering design process to solve it. Let’s get started!

This activity is intended as an introduction to the engineering design cycle. It is meant to be relatable to students and serve as a jumping off point for future engineering design work.

Engineers follow the steps of the engineering design process to guide them as they solve problems. The steps shown in Figure 1 are:

Ask: identify the need & constraints

Identify and define the problem. Who does the problem affect? What needs to be accomplished? What is the overall goal of the project?
Identify the criteria and constraints. The criteria are the requirements the solution must meet, such as designing a bag to hold at least 10 lbs. Constraints are the limitations and restrictions on a solution, such as a maximum budget or specific dimensions.

Research the problem

Learn everything you can about the problem. Talk to experts and/or research what products or solutions already exist.
If working for a client, such as designing new filters for a drinking water treatment plant, talk with the client to determine the needs and wants.

Imagine: develop possible solutions

Brainstorm ideas and come up with as many solutions as possible. Wild and crazy ideas are welcome! Encourage teamwork and building on ideas.

Plan: select a promising solution

Consider the pros and cons of all possible solutions, keeping in mind the criteria and constraints.
Choose one solution and make a plan to move forward with it.

Create: build a prototype

Create your chosen solution! Push for creativity, imagination and excellence in the design.

Test and evaluate prototype

Test out the solution to see how well it works. Does it meet all the criteria and solve the need? Does it stay within the constraints? Talk about what worked during testing and what didn’t work. Communicate the results and get feedback. What could be improved?

Improve: redesign as needed

Optimize the solution. Redesign parts that didn’t work, and test again.
Iterate! Engineers improve their ideas and designs many times as they work towards a solution.

Some depictions of the engineering design process delineate a separate step—communication. In the Figure 1 graphic, communication is considered to be incorporated throughout the process. For this activity, we call out a final step— communicate the solution —as a concluding stage to explain to others how the solution was designed, why it is useful, and how they might benefit from it. See the diagram on slide 3.

For another introductory overview of engineering and design, see the What Is Engineering? What Is Design? lesson and/or show students the What Is Engineering? video.

Before the Activity

Make copies of the five-page Marisol Case Study , one per student, and the Group Leader Discussion Sheet , one per group.
Be ready to show the class the Introduction to the Engineering Design Cycle Presentation , a PowerPoint® file.

With the Students

As a pre-activity assessment, spend a few minutes asking students the questions provided in the Assessment section.
Present the Introduction/Motivation content to the class, which includes using the slide presentation to introduce students to the engineering design cycle. Throughout, ask for their feedback, for example, any criteria or constraints that they would add, other design ideas or modifications, and so forth.
Divide the class into groups of four. Ask each team to elect a group leader. Hand out the case study packets to each student. Provide each group leader with a discussion sheet.
In their groups, have students work through the case study together.
Alert students to the case study layout with its clearly labeled “stop” points, and direct them to just read section by section, not reading beyond those points.
Suggest that students either taking turns reading each section aloud or read each section silently.
Once all students in a group have read a section, the group leader refers to the discussion sheet and asks its questions of the group, facilitating a discussion that involves every student.
Encourage students to annotate the case study as they like; for example, they might note in the margins Marisol’s stage in the design process at various points.
As students work in their groups, walk around the classroom and encourage group discussion. Ensure that each group member contributes to the discussion and that group members are focused on the same section (no reading ahead).
After all teams have finished the case study and its discussion questions, facilitate a class discussion about how Marisol used the engineering design cycle. This might include referring back to questions 4 and 5 in “Stop 5” to discuss remaining questions about the case study and relate the case study example back to the community problems students suggested in the pre-activity assessment.
Administer the post-activity assessment.

brainstorming: A team creativity activity with the purpose to generate a large number of potential solutions to a design challenge.

constraint: A limitation or restriction. For engineers, design constraints are the requirements and limitations that the final design solutions must meet. Constraints are part of identifying and defining a problem, the first stage of the engineering design cycle.

criteria: For engineers, the specifications and requirements design solutions must meet. Criteria are part of identifying and defining a problem, the first stage of the engineering design cycle.

develop : In the engineering design cycle, to create different solutions to an engineering problem.

engineering: Creating new things for the benefit of humanity and our world. Designing and building products, structures, machines and systems that solve problems. The “E” in STEM.

engineering design process: A series of steps used by engineering teams to guide them as they develop new solutions, products or systems. The process is cyclical and iterative. Also called the engineering design cycle.

evaluate: To assess something (such as a design solution) and form an idea about its merit or value (such as whether it meets project criteria and constraints).

optimize: To make the solution better after testing. Part of the engineering design cycle.

Pre-Activity Assessment

Intro Discussion: To gauge how much students already know about the activity topic and start students thinking about potential design problems in their everyday lives, facilitate a brief class discussion by asking students the following questions:

What do engineers do? (Example possible answers: Engineers design things that help people, they design/build/create new things, they work on computers, they solve problems, they create things that have never existed before, etc.)
What are some problems in your home, school or community that could be solved through engineering? (Example possible answers: It is too dark in a community field/park at night, it is hard to carry shopping bags in grocery store carts, the dishwasher does not clean the dishes well, we spend too much time trying to find shoes—or other items—in the house/garage/classroom, etc.)
How do engineers solve problems? (Example possible answers: They build new things, design new things, etc. If not mentioned, introduce students to the idea of the engineering design cycle. Liken this to how research scientists are guided by the steps of the scientific method.)

Activity Embedded Assessment

Small Group Discussions: As students work, observe their group discussions. Make sure the group leaders go through all the questions for each section, and that each group member contributes to the discussions.

Post-Activity Assessment

Marisol’s Design Process: Provide students with writing paper and have them write “Marisol’s Design Process” at the top. Direct them to clearly write out the steps that Marisol went through as they designed and completed their locker organizer design and label them according to where they fit in the engineering design cycle. For example, “Marisol had to jump back to avoid objects falling out of their locker” and they stated a desire to “wanted to find a way to organize their locker” both illustrate the “identifying the problem” step.

Which part of the engineering design cycle is Marisol working on as they design an organizer?
Why is it important to identify the criteria and constraints of a project before building and testing a prototype? (Example possible answers: So that the prototype will be the right size, so that you do not go over budget, so that it will solve the problem, etc.)
Why do engineers improve and optimize their designs? (Example possible answers: To make it work better, to fix unexpected problems that come up during testing, etc.)

To make this a more hands-on activity, have students design and build their own locker organizers (or other solutions to real-life problems they identified) in tandem with the above-described activity, incorporating the following changes/additions to the process:

Before the activity: Inform students that they will be undertaking an engineering design challenge. Without handing out the case study packet, introduce students to Marisol’s problem: a disorganized locker. Ask students to bring materials from home that they think could help solve this problem. Then, gather assorted materials (wood and fabric scraps, craft materials, tape, glue, etc.) to provide for this challenge, giving each material a cost (for example, wood pieces cost 50¢, fabric costs 25¢, etc.) and write these on the board or on paper to hand out to the class.
Present the Introduction/Motivation content and slides to introduce students to the engineering design process (as described above). Then have students go through the steps of the engineering design process to create a locker organizer for Marisol. Inform them Marisol has only $3 to spend on an organizer, so they must work within this budget constraint. As a size constraint, tell students the locker is 32 inches tall, 12 inches wide and 9.5 inches deep. (Alternatively, have students measure their own lockers and determine the size themselves.)
As students work, ask them some reflection questions such as, “Which step of the engineering design process are you working on?” and “Why have you chosen that solution?”
Let groups present their organizers to the class and explain the logic behind their designs.
Next, distribute the case study packet and discussion sheets to the student groups. As the teams read through the packet, encourage them to discuss the differences between their design solutions and Marisol’s. Mention that in engineering design there is no one right answer; rather, many possible solutions may exist. Multiple designs may be successful in imagining and fabricating a solution that meets the project criteria and constraints.

Engineering Design Process . 2014. TeachEngineering, Web. Accessed June 20, 2017. https://www.teachengineering.org/k12engineering/designprocess

Contributors

Supporting program, acknowledgements.

This material is based upon work supported by the National Science Foundation CAREER award grant no. DRL 1552567 (Amy Wilson-Lopez) titled, Examining Factors that Foster Low-Income Latino Middle School Students' Engineering Design Thinking in Literacy-Infused Technology and Engineering Classrooms. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Last modified: October 26, 2023

Understanding the engineering design process

Last updated

5 March 2024

Reviewed by

Mary Mikhail

To avoid the likelihood of failure, experienced engineers and designers use a structured decision-making process during the product innovation and development phase. This engineering design process facilitates problem-solving through:

Innovative thinking

Learning from failure

Using the engineering design process, you can identify how your products can solve problems and enable you to troubleshoot issues that could prevent the end product from delivering ideal results.

In this article, we'll explain exactly what the engineering design process is and provide a step-by-step guide for using it for the development of your next product.

What is the engineering design process?

The engineering design process is an iterative process of brainstorming, troubleshooting, and developing a product to solve a specific problem. The steps in the process encourage open-ended problem-solving and learning from failure.

When product designers use the engineering design process, they apply engineering experience, industry knowledge, and technology to design and build solutions for specific problems.

While the process follows a defined pattern, it is iterative to yield the best results. Some steps may be completed out of order or repeated to eliminate issues or examine areas of concern. The engineering design process is commonly used on any project that requires designing, building, and testing a product.

The principles of good engineering design

There are multiple variations of the engineering design process. However, good engineering design incorporates specific underlying principles.

These principles create a foundation to guide the engineering design process:

Engineering is a process with defined steps to provide the best solution to a specific problem.

The design process is iterative, requiring some steps to be repeated or carried out in a nonsequential format.

Analyzing and evaluating different solutions is an essential part of the process, to identify different strengths and weaknesses and follow the design specifications.

Failure is expected and used as a learning tool during the process.

Why is engineering design important for innovation?

The engineering design process was developed to create products that meet the needs of a target audience. By taking a structured approach to design that follows a defined path from problem to solution, engineers can research multiple ideas to determine which solution is most viable.

The process is iterative and flexible to encourage creativity. These features combine to create a method that produces innovative thinking and multiple solutions to complex problems.

Benefits of the engineering design process

The engineering design process turns ideas into practical solutions. This structured approach that cultivates creativity and breeds innovation brings several benefits.

Improved product quality

The engineering product design process follows specific steps to ensure every possibility is explored. Since the process promotes problem-solving, working in iterations, and extensive testing, the most innovative ideas bubble to the top for further development.

Collaboration

By using tactics like brainstorming, group thinking, prototyping, and testing, you encourage collaboration between various stakeholders, including:

Manufacturers

Multiple experts working toward a common goal can help you eliminate possible issues or shortfalls before a product is in the hands of customers.

Cost savings

By working with multiple ideas and identifying potential design flaws early in the process, you can greatly reduce the risk of a product going all the way to completion with flaws. Testing throughout the process addresses errors in real time and keeps rework to a minimum.

Faster time to market

Since the process requires teams to often repeat steps multiple times, you may be concerned about the development phase being prolonged. However, the ability to identify potential issues early reduces overall project duration, as less time is spent on revisions later on.

Increased innovation

The engineering design process is centered around creative thinking to promote the best solutions for a specific problem. It is typically carried out by multiple team members and encourages the use of failed projects as stepping stones to a better solution.

A step-by-step guide to the engineering design process

The engineering design process is designed to be versatile to meet the requirements of different types of projects. As such, the process can be adapted to include fewer or more steps.

It's also important to remember that the process is iterative, and steps may be repeated as needed to achieve a specific objective.

These steps cover the basics of the engineering design process.

1. Define the problem you want to solve with your product

To develop an effective solution, you must clearly define the problem, any known constraints to solving it, and the end user's needs.

Once you've identified the problem, clarify why it's a problem and the size of the audience who will be interested in a solution. Examine the problem from different viewpoints to ensure you understand its impact.

2. Ask questions and conduct relevant research

Dig deeper into the reason for the problem and whether potential solutions already exist. Ask these questions to guide your research:

Why is this a problem?

Why is it important to develop a solution to this problem?

Who can the right solution help?

Do solutions already exist? If so, why aren't they being used, or how is our solution going to be better?

Are there financial, legal, technical, or other restraints that could introduce new requirements for the solution?

3. Imagine possible solutions

Once you have all the information to hand, brainstorm a list of possible solutions.

Generate as many ideas as possible, including unconventional ones. Involve industry experts, employees, experienced professionals, and other stakeholders in the brainstorming process to get multiple perspectives.

Combine all prospective solutions into a list for consideration.

Examine your list and research each proposed solution. List the pros and cons of each solution to rank your list in order of viability.

Eliminate solutions that are less viable or have too many flaws. Continue to narrow down the list until you arrive at the ideal solution.

Draw up a product design to solve the problem while addressing constraints and potential issues that may arise during the build.

5. Develop a prototype

Use your plan to create an early prototype of your product. The prototype should be designed with cost-effectiveness in mind and can be made from different materials than the final product. It's also common to leave finishing touches off early prototypes so that you can concentrate on the overall concept.

Once you've created a usable prototype, it's time to conduct testing. Run a series of tests to determine how well the product works, not only in normal operating conditions but also in different conditions that introduce extra stress.

Use the tests to simulate potential real-life scenarios. Even if the prototype is functional, it may not meet all your expectations. Note where the product falls short, so improvements can be made during subsequent iterations.

7. Make improvements

If the prototype is a complete failure, you have choices to make. You can either revisit some of your earlier solutions or brainstorm ideas that will eliminate the problems.

If your prototype didn't meet all your expectations, it can still be called a success. Use your notes to investigate the areas where the prototype under-performed and consider ways to improve these specific issues.

Improve upon your existing prototype or design a new iteration of the prototype and test your product again. Repeat the testing and improvement process as many times as necessary to yield the best possible product.

8. Communicate the results

The final stage of the engineering design process is to share your results. You can create reports, presentations, and displays as needed to share the functionality of your product and why it's the best solution to the problem at hand.

Your documentation will be used to share the product's value with stakeholders and enable the finished product to be manufactured to the required standards. You may need to participate in meetings and presentations to share the results of the engineering design process with managers, engineers, board directors, shareholders, and even potential customers.

Once you have the go-ahead from all decision-makers, you’re ready to build.

Engineering design process examples

The engineering design process can be applied to a broad range of products. Let’s look at a couple of examples.

Developing a new gaming app

Let’s imagine you’d like to develop a new app in the form of a game.

Define the problem: What is the problem that your app will solve? Boredom? Lack of mental stimulation? Kids needing to be kept occupied? Parents needing to unwind? Relieving anxiety?

Research: How widespread is this problem? Find out what games are already available that solve this problem. Do they do it well, or is there a gap in the market for a game that will do it better? Are there any good reasons not to develop such a game?

Imagine solutions: With your team and relevant stakeholders, brainstorm as many games as possible. Let your imagination run riot!

Plan: List the pros and cons of each of your game ideas. Narrow down the list and plan a design for the one you decide to go ahead with.

Develop a prototype: The prototype doesn’t need to be complex, just with enough elements to give testers an idea of the game and how it can solve the problem you originally pinpointed.

Test: Find testers to try out your prototype game. Gather feedback from them.

Make improvements: Adjust your prototype based on the feedback from testers. Return to the testing step and keep repeating these two stages (testing and improving) until you have the best possible version of the game.

Share results: Create a report or presentation to communicate your decisions to stakeholders. Convince them of why this is the best solution. Once your game idea is signed off, it’s time to start developing the actual product!

Creating a kitchen appliance

There are so many gadgets available to make our lives easier in the kitchen, and people always seem to want more! Imagine you’d like to be the next big thing in the world of kitchen appliances.

Define the problem: What is the problem that your appliance will solve? Will it save time, effort, or money (or all three?) Will it reduce mess? Maybe it will increase safety in the kitchen or make it easier to create delicious meals.

Research: How widespread is the problem you’re trying to solve? Find out what appliances are already available that solve this problem. Do they do it well, or is there a gap in the market for a better appliance? Are there any good reasons not to create this appliance?

Imagine solutions: Brainstorm as many appliance solutions as possible. Be creative. Nothing is impossible at this stage!

Plan: What advantages and disadvantages are there for each of your appliance ideas? Narrow down the list of possible appliances, and plan a design for your favorite one.

Develop a prototype: This doesn’t need to be made from the same materials as the final appliance, nor does it need to have the same functionality. It needs to be enough for the testers to get an idea of the appliance and how it will solve a particular problem in the kitchen.

Test: Gather feedback from people who are willing to test your prototype appliance.

Make improvements: Adjust your prototype appliance based on the feedback from testers. Return to the testing step and keep repeating these two stages (testing and improving) until your prototype appliance receives no more negative feedback.

Share results: Tell your stakeholders about your appliance through a report or presentation. Convince them of why this is the best solution. Once it is signed off, you can start the production phase.

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7 Engineering Challenges Design Thinking Can Help Solve

19 Jan 2023

Several challenges face the engineering industry. Addressing them requires innovative solutions and structured processes, such as design thinking.

If you’re an engineer who wants to develop business skills , here's an overview of design thinking and seven engineering challenges it can help solve.

What Is Design Thinking?

Design thinking is one of the most effective approaches to problem-solving. It’s a solutions-based methodology focused on human-centered design and observing problems with empathy.

In the online course Design Thinking and Innovation , Harvard Business School Dean Srikant Datar structures the process using a four-stage framework. The stages are:

Graphic showing design thinking's four stages: clarify, ideate, develop, and implement

In the clarification stage, you observe a situation or challenge without bias and frame your findings in the form of a problem statement.

“Before you begin to generate innovative solutions for your own design problem, you must always think hard about how you’re going to frame that problem,” Datar says in the course.

Reframing the problem as a question is an excellent way to do this. For example, using "how might we" instead of "the problem is" can encourage empathy in the design process and shift your mindset toward potential solutions.

These questions are particularly important when considering empathetic design. According to the Harvard Business Review , engineers who put themselves in their audience's shoes while designing often develop innovative products . By understanding your audience’s unexpressed needs, you can effectively leverage your technical knowledge to create innovative solutions to previously unknown problems.

Once you've made your observations, you can explore potential solutions. The ideate stage is for divergent thinking—the process of exploring as many ideas as possible. It involves:

Finding and categorizing similarities in users' pain points
Considering the resources available to you and how you can use them to solve a problem
Brainstorming potential solutions

Creativity and an open mind are vital at this stage. As you explore ideas, they can highlight other problems you were unaware of.

The development stage focuses on turning your ideas into workable prototypes. For ideas to be innovative, they must be both new and useful ; many, though creative, aren't feasible.

"As you prototype concepts in phase three, you may discover results that force you to return to phases one and two to reframe your question," Datar says in Design Thinking and Innovation .

This iteration can occur in any of the four stages because each involves a different level of exploration that highlights new problems, questions, or solutions. This isn't cause for discouragement.

"Do not think of this as a setback,” Datar says in the course. “Iterating on solutions is a normal and expected result of design thinking.”

Design thinking’s ultimate objective is finding effective, workable solutions. The implementation phase involves finalizing developments and communicating their value to stakeholders.

This final stage can be challenging for many engineers. Since their work is so technical, it’s sometimes difficult for stakeholders to understand their impact on the organization. As a result, engineers should develop effective communication skills to ensure their ideas are implemented.

The Importance of Design Thinking in Engineering

Design thinking is a valuable skill for engineers to learn for several reasons. For one, engineering positions are among the most common occupations requiring design thinking skills .

Since engineers are often responsible for solving complex problems, it’s easy to get lost in the details and set creative problem-solving skills aside. Creativity in business is beneficial because it:

Encourages innovation
Boosts productivity
Allows for adaptability
Fosters growth

Graphic listing the benefits of creativity in business

Leveraging design thinking skills to pursue innovation not only helps professionals find creative solutions but identify business opportunities , evaluate market needs , and design new products and services.

Engineers’ responsibilities can vary. Whether creating new products or maintaining existing ones, engineering revolves around design . For this reason, a systematic approach is highly valuable when encountering industry challenges.

7 Engineering Challenges Design Thinking Can Solve

Some of the challenges engineers often face include:

Identifying obscure problems
Overcoming cognitive fixedness
Designing sustainable innovations
Addressing the skilled labor shortage
Encouraging diversity
Keeping up with advancing technology
Overcoming status-quo bias

Here’s an overview of how design thinking can help solve these problems.

1. Identifying Obscure Problems

Engineers often encounter problems that are difficult to identify. As a result, it can be easy for them to jump to conclusions based on preexisting knowledge of a design or situation. Datar discourages this in Design Thinking and Innovation .

"Whenever you have a difficult problem, you tend to solve the fringes of it,” Datar says. “But try and go for the most important part that you need to solve."

For example, if you're trying to remove a major obstacle preventing a project’s completion, you might be tempted to search for a cause equal in scope to its impact. However, some of the biggest design problems can be caused by something as simple as a misplaced hyphen or a loose screw. Often, the best approach is to consider the bigger picture. Is there anything in the design you don't understand?

The clarification stage in the design thinking framework encourages you to obtain insights through unbiased observation. An effective tool to accomplish this is journey mapping , which involves creating a chronological visual timeline of everything you know about a problem.

According to Design Thinking and Innovation , the three steps to developing a journey map are:

Creating observations about the user's journey
Writing those observations on a timeline
Organizing the observations into different stages

Creating a timeline of events can help identify when a problem occurs, as well as what precedes and follows it. This can enable you to narrow down its cause.

2. Overcoming Cognitive Fixedness

Cognitive fixedness is a mindset that assumes there's just one way to accomplish tasks. It considers every situation through the lens of past decisions. Thinking "if it worked in the past, it'll work now" is easy to follow, especially in the engineering industry, where replicating past successes is often the best way to proceed.

For example, while new technology trends can succeed in the market because of their innovative features, incorporating those features into an existing design might not be feasible—and even prevent you from meeting critical deadlines. Furthermore, in areas with high risk to human life—such as submarine design—it may be advisable to incorporate technology that’s proven effective before creating something new.

While caution is important, cognitive fixedness can prevent innovation, resulting in obsolescence. You must strike a balance between the operational and the innovation worlds.

The difference between the two worlds is described in Design Thinking and Innovation :

The operational world represents a business’s routine procedures.
The innovation world facilitates open-endedly exploring ideas.

Although the operational world is important, it can result in cognitive fixedness and prevent ideas’ progression. If you're struggling to overcome cognitive fixedness—whether your own or someone else's—consider why there's an unwillingness to change to determine the next steps.

3. Designing Sustainable Innovations

Climate change is a pressing issue impacting businesses around the globe . An increasing number of organizational leaders are addressing it by focusing on the triple bottom line . According to the HBS Online course Sustainable Business Strategy , the triple bottom line considers:

Profit: Satisfying shareholders and producing a profit
People: Impacting society in a positive, measurable way
The planet: Making a positive impact on the environment

By reframing problems and pursuing workable solutions that don't sacrifice profit, you can effectively incorporate sustainability into business strategies .

Design Thinking and Innovation | Uncover creative solutions to your business problems | Learn More

4. Addressing the Skilled Labor Shortage

The United States is experiencing a shortage of engineers , which has put a strain on employers hoping to hire qualified candidates in a shrinking market.

Consider how you'd approach this challenge from a design thinking perspective. Clarifying the problem might highlight opportunities you didn't previously think of. For instance, companies such as Google and Microsoft have invested in science, technology, engineering, and math (STEM) education , enabling more people to pursue careers in those industries.

Other companies have sought ways to attract engineering talent. It can be easy to draw candidates by raising salaries or increasing benefits, but many engineers aren't comfortable working for organizations that harm the environment. Your firm should consider adopting a sustainable business strategy that could benefit the planet and attract qualified applicants.

5. Encouraging Diversity

Engineering has historically been a male-dominated field. One of the primary causes of this imbalance is the workplace stereotype that STEM careers are masculine. This has resulted in implicit—and often direct—discouragement of women from pursuing STEM careers.

In the context of design thinking, clarifying and reframing the problem might result in questions like, "How can we empower more women to pursue STEM careers?"

Through exploring potential solutions, you may discover that encouraging and empowering a diverse population to pursue engineering can help address other challenges, such as the skilled labor shortage.

6. Keeping Up with Advancing Technology

Technology is continuously advancing; companies that fail to adapt might get left behind. For example, Blackberry was once one of the fastest-growing smartphone companies in the world. Yet, its products became obsolete when the company refused to adopt touch-screen technology. This resulted in Blackberry losing 90 percent of its market share between 2009 and 2013.

Design thinking encourages continual awareness to avoid these downward trends. Learning how to recognize opportunities and communicate them to others can prevent a business from falling behind.

7. Overcoming Status-Quo Bias

Resistance to change doesn't just occur within an organization—it happens among customers, too. This is known as status-quo bias , which is a challenge you must address during implementation.

The challenge is how to retain existing customers while appealing to the current market and acquiring new ones. Avoid assuming users will understand a design change you’ve implemented just because it makes sense to you.

According to Datar in Design Thinking and Innovation , you should consider three views during the implementation phase:

The developer's view: The designer with knowledge and understanding of a design's utility and benefits
The neutral view: Someone who doesn't have a preexisting opinion about the design
Stakeholders' view: Existing customers and users who have existing opinions based on the status quo

Learning how to overcome status-quo bias is critical to successful innovation.

Which HBS Online Entrepreneurship and Innovation Course is Right for You? | Download Your Free Flowchart

Improving Your Design Thinking Skills

Whether encountering one of the engineering challenges mentioned above or something more niche, design thinking can be a valuable tool for solving them.

Learning about the process and its business applications can enable you to climb the corporate ladder and make an impact on your organization.

Ready to learn the tools you need to innovate? Enroll in our online certificate course Design Thinking and Innovation —one of our entrepreneurship and innovation courses —and develop in-demand skills that can benefit your engineering career. If you aren’t sure which HBS Online course is right for you, download our free flowchart to explore your options.

About the Author

What is Chemical and Biological Engineering?
Engineering problem solving
Error and uncertainty
Process variables
Process Fundamentals
Material Balances
Reacting systems
Reaction kinetics
Reactor design
Bioreactors
Fluids and fluid flow
Mass transfer
Energy balances
Heat transfer
Heat exchangers
Mechanical energy balances
Process safety
Engineering ethics
Sustainability
Engineering in a global context
How ‘good’ a solution do you need
Steps in solving well-defined engineering process problems, including textbook problems
« What is Chemi...
Teamwork »

Engineering Problem Solving ¶

Some problems are so complex that you have to be highly intelligent and well-informed just to be undecided about them. —Laurence J. Peter

Steps in solving ‘real world’ engineering problems ¶

The following are the steps as enumerated in your textbook:

Collaboratively define the problem

List possible solutions

Evaluate and rank the possible solutions

Develop a detailed plan for the most attractive solution(s)

Re-evaluate the plan to check desirability

Implement the plan

Check the results

A critical part of the analysis process is the ‘last’ step: checking and verifying the results.

Depending on the circumstances, errors in an analysis, procedure, or implementation can have significant, adverse consequences (NASA Mars orbiter crash, Bhopal chemical leak tragedy, Hubble telescope vision issue, Y2K fiasco, BP oil rig blowout, …).

In a practical sense, these checks must be part of a comprehensive risk management strategy.

My experience with problem solving in industry was pretty close to this, though encumbered by numerous business practices (e.g., ‘go/no-go’ tollgates, complex approval processes and procedures).

In addition, solving problems in the ‘real world’ requires a multidisciplinary effort, involving people with various expertise: engineering, manufacturing, supply chain, legal, marketing, product service and warranty, …

Exercise: Problem solving

Step 3 above refers to ranking of alternatives.

Think of an existing product of interest.

What do you think was ranked highest when the product was developed?

Consider what would have happened if a different ranking was used. What would have changed about the product?

Brainstorm ideas with the students around you.

Defining problems collaboratively ¶

Especially in light of global engineering , we need to consider different perspectives as we define our problem. Let’s break the procedure down into steps:

Identify each perspective that is involved in the decision you face. Remember that problems often mean different things in different perspectives. Relevant differences might include national expectations, organizational positions, disciplines, career trajectories, etc. Consider using the mnemonic device “Location, Knowledge, and Desire.”

Location : Who is defining the problem? Where are they located or how are they positioned? How do they get in their positions? Do you know anything about the history of their positions, and what led to the particular configuration of positions you have today on the job? Where are the key boundaries among different types of groups, and where are the alliances?

Knowledge : What forms of knowledge do the representatives of each perspective have? How do they understand the problem at hand? What are their assumptions? From what sources did they gain their knowledge? How did their knowledge evolve?

Desire : What do the proponents of each perspective want? What are their objectives? How do these desires develop? Where are they trying to go? Learn what you can about the history of the issue at hand. Who might have gained or lost ground in previous encounters? How does each perspective view itself at present in relation to those it envisions as relevant to its future?

As formal problem definitions emerge, ask “Whose definition is this?” Remember that “defining the problem clearly” may very well assert one perspective at the expense of others. Once we think about problem solving in relation to people, we can begin to see that the very act of drawing a boundary around a problem has non-technical, or political dimensions, depending on who controls the definition, because someone gains a little power and someone loses a little power.

Map what alternative problem definitions mean to different participants. More than likely you will best understand problem definitions that fit your perspective. But ask “Does it fit other perspectives as well?” Look at those who hold Perspective A. Does your definition fit their location, their knowledge, and their desires? Now turn to those who hold Perspective B. Does your definition fit their location, knowledge, and desires? Completing this step is difficult because it requires stepping outside of one’s own perspective and attempting to understand the problem in terms of different perspectives.

To the extent you encounter disagreement or conclude that the achievement of it is insufficient, begin asking yourself the following: How might I adapt my problem definition to take account of other perspectives out there? Is there some way of accommodating myself to other perspectives rather than just demanding that the others simply recognize the inherent value and rationality of mine? Is there room for compromise among contrasting perspectives?

How ‘good’ a solution do you need ¶

There is also an important aspect of real-world problem solving that is rarely articulated and that is the idea that the ‘quality’ of the analysis and the resources expended should be dependent on the context.

This is difficult to assess without some experience in the particular environment.

How ‘Good’ a Solution Do You Need?

Some rough examples:

10 second answer (answering a question at a meeting in front of your manager or vice president)

10 minute answer (answering a quick question from a colleague)

10 hour answer (answering a request from an important customer)

10 day answer (assembling information as part of a trouble-shooting team)

10 month answer (putting together a comprehensive portfolio of information as part of the design for a new $200,000,000 chemical plant)

Steps in solving well-defined engineering process problems, including textbook problems ¶

Essential steps:

Carefully read the problem statement (perhaps repeatedly) until you understand exactly the scenario and what is being asked.

Translate elements of the word problem to symbols. Also, look for key words that may convey additional information, e.g., ‘steady state’, ‘constant density’, ‘isothermal’. Make note of this additional information on your work page.

Draw a diagram. This can generally be a simple block diagram showing all the input, output, and connecting streams.

Write all known quantities (flow rates, densities, etc.) from step 2 in the appropriate locations on, or near, the diagram. If symbols are used to designate known quantities, include those symbols.

Identify and assign symbols to all unknown quantities and write them in the appropriate locations on, or near, the diagram.

Construct the relevant equation(s). These could be material balances, energy balances, rate equations, etc.

Write down all equations in their general forms. Don’t simplify anything yet.

Discard terms that are equal to zero (or are assumed negligible) for your specific problem and write the simplified equations.

Replace remaining terms with more convenient forms (because of the given information or selected symbols).

Construct equations to express other known relationships between variables, e.g., relationships between stoichiometric coefficients, the sum of species mass fractions must be one.

Whenever possible, solve the equations for the unknown(s) algebraically .

Convert the units of your variables as needed to have a consistent set across your equations.

Substitute these values into the equation(s) from step 7 to get numerical results.

Check your answer.

Does it make sense?

Are the units of the answer correct?

Is the answer consistent with other information you have?

Exercise: Checking results

How do you know your answer is right and that your analysis is correct?

This may be relatively easy for a homework problem, but what about your analysis for an ill-defined ‘real-world’ problem?

Engineering design processes, problem solving and creativity

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Engineering Design and Problem Solving

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The Engineering Design and Problem-Solving course is the creative process of solving problems by identifying needs and then devising solutions. The solution may be a product, technique, structure, or process depending on the problem. Science aims to understand the natural world, while engineering seeks to shape this world to meet human needs and wants. Engineering design takes into consideration limiting factors or "design under constraint." Various engineering disciplines address a broad spectrum of design problems using specific concepts from the sciences and mathematics to derive a solution.

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Scope and Sequence

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Engineering Design and Problem Solving course scope and sequence within the Science, Technology, Engineering, and Mathematics Career Cluster® summarizes the content to be taught, and one possible order for teaching the units of instruction. A brief description of each unit and the corresponding TEKS are included. This scope and sequence may be adapted or adopted by the local education agency.

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Based on the House Bill 5 Foundation High School Program, the Engineering program of study within the Science, Technology, Engineering, and Mathematics Career Cluster® provides helpful information, including the core courses and career-related electives in high school that will help prepare students for their career goals. This document is designed for students, but can also be used by administrators, counselors, teachers, business and industry representatives, and parents.

Based on the House Bill 5 Foundation High School Program, the Physical Science program of study within the Science, Technology, Engineering, and Mathematics Career Cluster® provides helpful information, including the core courses and career-related electives in high school that will help prepare students for their career goals. This document is designed for students, but can also be used by administrators, counselors, teachers, business and industry representatives, and parents.

Unit 1: Exploration of the STEM Field of Engineering Design

Lesson plans are currently not available. Review the scope and sequence document, TEKS, and available lesson plans, to determine which additional lesson plans to locally develop.

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Unit 3: use of scientific method to develop a solution, unit 4: safety precautions, unit 5: application of problem solving skills, unit 6: solve engineering design problems.

In this lesson, students will explain the concept of engineering design, apply engineering design concepts to a problem scenario, practice sketching technical drawings in their engineering notebooks, and reinforce collaborative and communication skills.

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Unit 7: Solve Engineering Design Problems

Unit 8: communication skills in the stem field, unit 9: formulating solutions, unit 10: employability skills, unit 11: teamwork in stem, unit 12: extended learning experience.

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Open access
Published: 10 April 2024

A hybrid particle swarm optimization algorithm for solving engineering problem

Jinwei Qiao 1 , 2 ,
Guangyuan Wang 1 , 2 ,
Zhi Yang 1 , 2 ,
Xiaochuan Luo 3 ,
Jun Chen 1 , 2 ,
Kan Li 4 &
Pengbo Liu 1 , 2

Scientific Reports volume 14 , Article number: 8357 ( 2024 ) Cite this article

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Computational science
Mechanical engineering

To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( ${f}_{1}-{f}_{13}$ ) with 3 kinds of dimensional spaces (Dim = 30,50,100) and 80% of the best optimal solutions for 10 fixed-multimodal benchmark functions. Also, the best design solutions are obtained by NDWPSO for all 3 classical practical engineering problems.

A clustering-based competitive particle swarm optimization with grid ranking for multi-objective optimization problems

Qianlin Ye, Zheng Wang, … Mengjiao Yu

A modified shuffled frog leaping algorithm with inertia weight

Zhuanzhe Zhao, Mengxian Wang, … Zhibo Liu

Appropriate noise addition to metaheuristic algorithms can enhance their performance

Kwok Pui Choi, Enzio Hai Hong Kam, … Weng Kee Wong

Introduction

In the ever-changing society, new optimization problems arise every moment, and they are distributed in various fields, such as automation control 1 , statistical physics 2 , security prevention and temperature prediction 3 , artificial intelligence 4 , and telecommunication technology 5 . Faced with a constant stream of practical engineering optimization problems, traditional solution methods gradually lose their efficiency and convenience, making it more and more expensive to solve the problems. Therefore, researchers have developed many metaheuristic algorithms and successfully applied them to the solution of optimization problems. Among them, Particle swarm optimization (PSO) algorithm 6 is one of the most widely used swarm intelligence algorithms.

However, the basic PSO has a simple operating principle and solves problems with high efficiency and good computational performance, but it suffers from the disadvantages of easily trapping in local optima and premature convergence. To improve the overall performance of the particle swarm algorithm, an improved particle swarm optimization algorithm is proposed by the multiple hybrid strategy in this paper. The improved PSO incorporates the search ideas of other intelligent algorithms (DE, WOA), so the improved algorithm proposed in this paper is named NDWPSO. The main improvement schemes are divided into the following 4 points: Firstly, a strategy of elite opposition-based learning is introduced into the particle population position initialization. A high-quality initialization matrix of population position can improve the convergence speed of the algorithm. Secondly, a dynamic weight methodology is adopted for the acceleration coefficients by combining the iterative map and linearly transformed method. This method utilizes the chaotic nature of the mapping function, the fast convergence capability of the dynamic weighting scheme, and the time-varying property of the acceleration coefficients. Thus, the global search and local search of the algorithm are balanced and the global search speed of the population is improved. Thirdly, a determination mechanism is set up to detect whether the algorithm falls into a local optimum. When the algorithm is “premature”, the population resets 40% of the position information to overcome the local optimum. Finally, the spiral shrinking mechanism combined with the DE/best/2 position mutation is used in the later iteration, which further improves the solution accuracy.

The structure of the paper is given as follows: Sect. “ Particle swarm optimization (PSO) ” describes the principle of the particle swarm algorithm. Section “ Improved particle swarm optimization algorithm ” shows the detailed improvement strategy and a comparison experiment of inertia weight is set up for the proposed NDWPSO. Section “ Experiment and discussion ” includes the experimental and result discussion sections on the performance of the improved algorithm. Section “ Conclusions and future works ” summarizes the main findings of this study.

Literature review

This section reviews some metaheuristic algorithms and other improved PSO algorithms. A simple discussion about recently proposed research studies is given.

Metaheuristic algorithms

A series of metaheuristic algorithms have been proposed in recent years by using various innovative approaches. For instance, Lin et al. 7 proposed a novel artificial bee colony algorithm (ABCLGII) in 2018 and compared ABCLGII with other outstanding ABC variants on 52 frequently used test functions. Abed-alguni et al. 8 proposed an exploratory cuckoo search (ECS) algorithm in 2021 and carried out several experiments to investigate the performance of ECS by 14 benchmark functions. Brajević 9 presented a novel shuffle-based artificial bee colony (SB-ABC) algorithm for solving integer programming and minimax problems in 2021. The experiments are tested on 7 integer programming problems and 10 minimax problems. In 2022, Khan et al. 10 proposed a non-deterministic meta-heuristic algorithm called Non-linear Activated Beetle Antennae Search (NABAS) for a non-convex tax-aware portfolio selection problem. Brajević et al. 11 proposed a hybridization of the sine cosine algorithm (HSCA) in 2022 to solve 15 complex structural and mechanical engineering design optimization problems. Abed-Alguni et al. 12 proposed an improved Salp Swarm Algorithm (ISSA) in 2022 for single-objective continuous optimization problems. A set of 14 standard benchmark functions was used to evaluate the performance of ISSA. In 2023, Nadimi et al. 13 proposed a binary starling murmuration optimization (BSMO) to select the effective features from different important diseases. In the same year, Nadimi et al. 14 systematically reviewed the last 5 years' developments of WOA and made a critical analysis of those WOA variants. In 2024, Fatahi et al. 15 proposed an Improved Binary Quantum-based Avian Navigation Optimizer Algorithm (IBQANA) for the Feature Subset Selection problem in the medical area. Experimental evaluation on 12 medical datasets demonstrates that IBQANA outperforms 7 established algorithms. Abed-alguni et al. 16 proposed an Improved Binary DJaya Algorithm (IBJA) to solve the Feature Selection problem in 2024. The IBJA’s performance was compared against 4 ML classifiers and 10 efficient optimization algorithms.

Improved PSO algorithms

Many researchers have constantly proposed some improved PSO algorithms to solve engineering problems in different fields. For instance, Yeh 17 proposed an improved particle swarm algorithm, which combines a new self-boundary search and a bivariate update mechanism, to solve the reliability redundancy allocation problem (RRAP) problem. Solomon et al. 18 designed a collaborative multi-group particle swarm algorithm with high parallelism that was used to test the adaptability of Graphics Processing Units (GPUs) in distributed computing environments. Mukhopadhyay and Banerjee 19 proposed a chaotic multi-group particle swarm optimization (CMS-PSO) to estimate the unknown parameters of an autonomous chaotic laser system. Duan et al. 20 designed an improved particle swarm algorithm with nonlinear adjustment of inertia weights to improve the coupling accuracy between laser diodes and single-mode fibers. Sun et al. 21 proposed a particle swarm optimization algorithm combined with non-Gaussian stochastic distribution for the optimal design of wind turbine blades. Based on a multiple swarm scheme, Liu et al. 22 proposed an improved particle swarm optimization algorithm to predict the temperatures of steel billets for the reheating furnace. In 2022, Gad 23 analyzed the existing 2140 papers on Swarm Intelligence between 2017 and 2019 and pointed out that the PSO algorithm still needs further research. In general, the improved methods can be classified into four categories:

Adjusting the distribution of algorithm parameters. Feng et al. 24 used a nonlinear adaptive method on inertia weights to balance local and global search and introduced asynchronously varying acceleration coefficients.

Changing the updating formula of the particle swarm position. Both papers 25 and 26 used chaotic mapping functions to update the inertia weight parameters and combined them with a dynamic weighting strategy to update the particle swarm positions. This improved approach enables the particle swarm algorithm to be equipped with fast convergence of performance.

The initialization of the swarm. Alsaidy and Abbood proposed 27 a hybrid task scheduling algorithm that replaced the random initialization of the meta-heuristic algorithm with the heuristic algorithms MCT-PSO and LJFP-PSO.

Combining with other intelligent algorithms: Liu et al. 28 introduced the differential evolution (DE) algorithm into PSO to increase the particle swarm as diversity and reduce the probability of the population falling into local optimum.

Particle swarm optimization (PSO)

The particle swarm optimization algorithm is a population intelligence algorithm for solving continuous and discrete optimization problems. It originated from the social behavior of individuals in bird and fish flocks 6 . The core of the PSO algorithm is that an individual particle identifies potential solutions by flight in a defined constraint space adjusts its exploration direction to approach the global optimal solution based on the shared information among the group, and finally solves the optimization problem. Each particle $i$ includes two attributes: velocity vector ${V}_{i}=\left[{v}_{i1},{v}_{i2},{v}_{i3},{...,v}_{ij},{...,v}_{iD},\right]$ and position vector ${X}_{i}=[{x}_{i1},{x}_{i2},{x}_{i3},...,{x}_{ij},...,{x}_{iD}]$ . The velocity vector is used to modify the motion path of the swarm; the position vector represents a potential solution for the optimization problem. Here, $j=\mathrm{1,2},\dots ,D$ , $D$ represents the dimension of the constraint space. The equations for updating the velocity and position of the particle swarm are shown in Eqs. ( 1 ) and ( 2 ).

Here ${Pbest}_{i}^{k}$ represents the previous optimal position of the particle $i$ , and ${Gbest}$ is the optimal position discovered by the whole population. $i=\mathrm{1,2},\dots ,n$ , $n$ denotes the size of the particle swarm. ${c}_{1}$ and ${c}_{2}$ are the acceleration constants, which are used to adjust the search step of the particle 29 . ${r}_{1}$ and ${r}_{2}$ are two random uniform values distributed in the range $[\mathrm{0,1}]$ , which are used to improve the randomness of the particle search. $\omega$ inertia weight parameter, which is used to adjust the scale of the search range of the particle swarm 30 . The basic PSO sets the inertia weight parameter as a time-varying parameter to balance global exploration and local seeking. The updated equation of the inertia weight parameter is given as follows:

where ${\omega }_{max}$ and ${\omega }_{min}$ represent the upper and lower limits of the range of inertia weight parameter. $k$ and $Mk$ are the current iteration and maximum iteration.

Improved particle swarm optimization algorithm

According to the no free lunch theory 31 , it is known that no algorithm can solve every practical problem with high quality and efficiency for increasingly complex and diverse optimization problems. In this section, several improvement strategies are proposed to improve the search efficiency and overcome this shortcoming of the basic PSO algorithm.

Improvement strategies

The optimization strategies of the improved PSO algorithm are shown as follows:

The inertia weight parameter is updated by an improved chaotic variables method instead of a linear decreasing strategy. Chaotic mapping performs the whole search at a higher speed and is more resistant to falling into local optimal than the probability-dependent random search 32 . However, the population may result in that particles can easily fly out of the global optimum boundary. To ensure that the population can converge to the global optimum, an improved Iterative mapping is adopted and shown as follows:

Here ${\omega }_{k}$ is the inertia weight parameter in the iteration $k$ , $b$ is the control parameter in the range $[\mathrm{0,1}]$ .

The acceleration coefficients are updated by the linear transformation. ${c}_{1}$ and ${c}_{2}$ represent the influential coefficients of the particles by their own and population information, respectively. To improve the search performance of the population, ${c}_{1}$ and ${c}_{2}$ are changed from fixed values to time-varying parameter parameters, that are updated by linear transformation with the number of iterations:

where ${c}_{max}$ and ${c}_{min}$ are the maximum and minimum values of acceleration coefficients, respectively.

The initialization scheme is determined by elite opposition-based learning . The high-quality initial population will accelerate the solution speed of the algorithm and improve the accuracy of the optimal solution. Thus, the elite backward learning strategy 33 is introduced to generate the position matrix of the initial population. Suppose the elite individual of the population is ${X}=[{x}_{1},{x}_{2},{x}_{3},...,{x}_{j},...,{x}_{D}]$ , and the elite opposition-based solution of $X$ is ${X}_{o}=[{x}_{{\text{o}}1},{x}_{{\text{o}}2},{x}_{{\text{o}}3},...,{x}_{oj},...,{x}_{oD}]$ . The formula for the elite opposition-based solution is as follows:

where ${k}_{r}$ is the random value in the range $(\mathrm{0,1})$ . ${ux}_{oij}$ and ${lx}_{oij}$ are dynamic boundaries of the elite opposition-based solution in $j$ dimensional variables. The advantage of dynamic boundary is to reduce the exploration space of particles, which is beneficial to the convergence of the algorithm. When the elite opposition-based solution is out of bounds, the out-of-bounds processing is performed. The equation is given as follows:

After calculating the fitness function values of the elite solution and the elite opposition-based solution, respectively, $n$ high quality solutions were selected to form a new initial population position matrix.

The position updating Eq. ( 2 ) is modified based on the strategy of dynamic weight. To improve the speed of the global search of the population, the strategy of dynamic weight from the artificial bee colony algorithm 34 is introduced to enhance the computational performance. The new position updating equation is shown as follows:

Here $\rho$ is the random value in the range $(\mathrm{0,1})$ . $\psi$ represents the acceleration coefficient and ${\omega }{\prime}$ is the dynamic weight coefficient. The updated equations of the above parameters are as follows:

where $f(i)$ denotes the fitness function value of individual particle $i$ and u is the average of the population fitness function values in the current iteration. The Eqs. ( 11 , 12 ) are introduced into the position updating equation. And they can attract the particle towards positions of the best-so-far solution in the search space.

New local optimal jump-out strategy is added for escaping from the local optimal. When the value of the fitness function for the population optimal particles does not change in M iterations, the algorithm determines that the population falls into a local optimal. The scheme in which the population jumps out of the local optimum is to reset the position information of the 40% of individuals within the population, in other words, to randomly generate the position vector in the search space. M is set to 5% of the maximum number of iterations.

New spiral update search strategy is added after the local optimal jump-out strategy. Since the whale optimization algorithm (WOA) was good at exploring the local search space 35 , the spiral update search strategy in the WOA 36 is introduced to update the position of the particles after the swarm jumps out of local optimal. The equation for the spiral update is as follows:

Here $D=\left|{x}_{i}\left(k\right)-Gbest\right|$ denotes the distance between the particle itself and the global optimal solution so far. $B$ is the constant that defines the shape of the logarithmic spiral. $l$ is the random value in $[-\mathrm{1,1}]$ . $l$ represents the distance between the newly generated particle and the global optimal position, $l=-1$ means the closest distance, while $l=1$ means the farthest distance, and the meaning of this parameter can be directly observed by Fig. 1 .

Spiral updating position.

The DE/best/2 mutation strategy is introduced to form the mutant particle. 4 individuals in the population are randomly selected that differ from the current particle, then the vector difference between them is rescaled, and the difference vector is combined with the global optimal position to form the mutant particle. The equation for mutation of particle position is shown as follows:

where ${x}^{*}$ is the mutated particle, $F$ is the scale factor of mutation, ${r}_{1}$ , ${r}_{2}$ , ${r}_{3}$ , ${r}_{4}$ are random integer values in $(0,n]$ and not equal to $i$ , respectively. Specific particles are selected for mutation with the screening conditions as follows:

where $Cr$ represents the probability of mutation, $rand\left(\mathrm{0,1}\right)$ is a random number in $\left(\mathrm{0,1}\right)$ , and ${i}_{rand}$ is a random integer value in $(0,n]$ .

The improved PSO incorporates the search ideas of other intelligent algorithms (DE, WOA), so the improved algorithm proposed in this paper is named NDWPSO. The pseudo-code for the NDWPSO algorithm is given as follows:

The main procedure of NDWPSO.

Comparing the distribution of inertia weight parameters

There are several improved PSO algorithms (such as CDWPSO 25 , and SDWPSO 26 ) that adopt the dynamic weighted particle position update strategy as their improvement strategy. The updated equations of the CDWPSO and the SDWPSO algorithm for the inertia weight parameters are given as follows:

where ${\text{A}}$ is a value in $(\mathrm{0,1}]$ . ${r}_{max}$ and ${r}_{min}$ are the upper and lower limits of the fluctuation range of the inertia weight parameters, $k$ is the current number of algorithm iterations, and $Mk$ denotes the maximum number of iterations.

Considering that the update method of inertia weight parameters by our proposed NDWPSO is comparable to the CDWPSO, and SDWPSO, a comparison experiment for the distribution of inertia weight parameters is set up in this section. The maximum number of iterations in the experiment is $Mk=500$ . The distributions of CDWPSO, SDWPSO, and NDWPSO inertia weights are shown sequentially in Fig. 2 .

The inertial weight distribution of CDWPSO, SDWPSO, and NDWPSO.

In Fig. 2 , the inertia weight value of CDWPSO is a random value in (0,1]. It may make individual particles fly out of the range in the late iteration of the algorithm. Similarly, the inertia weight value of SDWPSO is a value that tends to zero infinitely, so that the swarm no longer can fly in the search space, making the algorithm extremely easy to fall into the local optimal value. On the other hand, the distribution of the inertia weights of the NDWPSO forms a gentle slope by two curves. Thus, the swarm can faster lock the global optimum range in the early iterations and locate the global optimal more precisely in the late iterations. The reason is that the inertia weight values between two adjacent iterations are inversely proportional to each other. Besides, the time-varying part of the inertial weight within NDWPSO is designed to reduce the chaos characteristic of the parameters. The inertia weight value of NDWPSO avoids the disadvantages of the above two schemes, so its design is more reasonable.

Experiment and discussion

In this section, three experiments are set up to evaluate the performance of NDWPSO: (1) the experiment of 23 classical functions 37 between NDWPSO and three particle swarm algorithms (PSO 6 , CDWPSO 25 , SDWPSO 26 ); (2) the experiment of benchmark test functions between NDWPSO and other intelligent algorithms (Whale Optimization Algorithm (WOA) 36 , Harris Hawk Algorithm (HHO) 38 , Gray Wolf Optimization Algorithm (GWO) 39 , Archimedes Algorithm (AOA) 40 , Equilibrium Optimizer (EO) 41 and Differential Evolution (DE) 42 ); (3) the experiment for solving three real engineering problems (welded beam design 43 , pressure vessel design 44 , and three-bar truss design 38 ). All experiments are run on a computer with Intel i5-11400F GPU, 2.60 GHz, 16 GB RAM, and the code is written with MATLAB R2017b.

The benchmark test functions are 23 classical functions, which consist of indefinite unimodal (F1–F7), indefinite dimensional multimodal functions (F8–F13), and fixed-dimensional multimodal functions (F14–F23). The unimodal benchmark function is used to evaluate the global search performance of different algorithms, while the multimodal benchmark function reflects the ability of the algorithm to escape from the local optimal. The mathematical equations of the benchmark functions are shown and found as Supplementary Tables S1 – S3 online.

Experiments on benchmark functions between NDWPSO, and other PSO variants

The purpose of the experiment is to show the performance advantages of the NDWPSO algorithm. Here, the dimensions and corresponding population sizes of 13 benchmark functions (7 unimodal and 6 multimodal) are set to (30, 40), (50, 70), and (100, 130). The population size of 10 fixed multimodal functions is set to 40. Each algorithm is repeated 30 times independently, and the maximum number of iterations is 200. The performance of the algorithm is measured by the mean and the standard deviation (SD) of the results for different benchmark functions. The parameters of the NDWPSO are set as: ${[{\omega }_{min},\omega }_{max}]=[\mathrm{0.4,0.9}]$ , $\left[{c}_{max},{c}_{min}\right]=\left[\mathrm{2.5,1.5}\right],{V}_{max}=0.1,b={e}^{-50}, M=0.05\times Mk, B=1,F=0.7, Cr=0.9.$ And, $A={\omega }_{max}$ for CDWPSO; ${[r}_{max},{r}_{min}]=[\mathrm{4,0}]$ for SDWPSO.

Besides, the experimental data are retained to two decimal places, but some experimental data will increase the number of retained data to pursue more accuracy in comparison. The best results in each group of experiments will be displayed in bold font. The experimental data is set to 0 if the value is below 10 –323 . The experimental parameter settings in this paper are different from the references (PSO 6 , CDWPSO 25 , SDWPSO 26 , so the final experimental data differ from the ones within the reference.

As shown in Tables 1 and 2 , the NDWPSO algorithm obtains better results for all 49 sets of data than other PSO variants, which include not only 13 indefinite-dimensional benchmark functions and 10 fixed-multimodal benchmark functions. Remarkably, the SDWPSO algorithm obtains the same accuracy of calculation as NDWPSO for both unimodal functions f 1 –f 4 and multimodal functions f 9 –f 11 . The solution accuracy of NDWPSO is higher than that of other PSO variants for fixed-multimodal benchmark functions f 14 -f 23 . The conclusion can be drawn that the NDWPSO has excellent global search capability, local search capability, and the capability for escaping the local optimal.

In addition, the convergence curves of the 23 benchmark functions are shown in Figs. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 and 19 . The NDWPSO algorithm has a faster convergence speed in the early stage of the search for processing functions f1-f6, f8-f14, f16, f17, and finds the global optimal solution with a smaller number of iterations. In the remaining benchmark function experiments, the NDWPSO algorithm shows no outstanding performance for convergence speed in the early iterations. There are two reasons of no outstanding performance in the early iterations. On one hand, the fixed-multimodal benchmark function has many disturbances and local optimal solutions in the whole search space. on the other hand, the initialization scheme based on elite opposition-based learning is still stochastic, which leads to the initial position far from the global optimal solution. The inertia weight based on chaotic mapping and the strategy of spiral updating can significantly improve the convergence speed and computational accuracy of the algorithm in the late search stage. Finally, the NDWPSO algorithm can find better solutions than other algorithms in the middle and late stages of the search.

Evolution curve of NDWPSO and other PSO algorithms for f1 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f2 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f3 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f4 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f5 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f6 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f7 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f8 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f9 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f10 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f11(Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f12 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f13 (Dim = 30,50,100).

Evolution curve of NDWPSO and other PSO algorithms for f14, f15, f16.

Evolution curve of NDWPSO and other PSO algorithms for f17, f18, f19.

Evolution curve of NDWPSO and other PSO algorithms for f20, f21, f22.

Evolution curve of NDWPSO and other PSO algorithms for f23.

To evaluate the performance of different PSO algorithms, a statistical test is conducted. Due to the stochastic nature of the meta-heuristics, it is not enough to compare algorithms based on only the mean and standard deviation values. The optimization results cannot be assumed to obey the normal distribution; thus, it is necessary to judge whether the results of the algorithms differ from each other in a statistically significant way. Here, the Wilcoxon non-parametric statistical test 45 is used to obtain a parameter called p -value to verify whether two sets of solutions are different to a statistically significant extent or not. Generally, it is considered that p ≤ 0.5 can be considered as a statistically significant superiority of the results. The p -values calculated in Wilcoxon’s rank-sum test comparing NDWPSO and other PSO algorithms are listed in Table 3 for all benchmark functions. The p -values in Table 3 additionally present the superiority of the NDWPSO because all of the p -values are much smaller than 0.5.

In general, the NDWPSO has the fastest convergence rate when finding the global optimum from Figs. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 and 19 , and thus we can conclude that the NDWPSO is superior to the other PSO variants during the process of optimization.

Comparison experiments between NDWPSO and other intelligent algorithms

Experiments are conducted to compare NDWPSO with several other intelligent algorithms (WOA, HHO, GWO, AOA, EO and DE). The experimental object is 23 benchmark functions, and the experimental parameters of the NDWPSO algorithm are set the same as in Experiment 4.1. The maximum number of iterations of the experiment is increased to 2000 to fully demonstrate the performance of each algorithm. Each algorithm is repeated 30 times individually. The parameters of the relevant intelligent algorithms in the experiments are set as shown in Table 4 . To ensure the fairness of the algorithm comparison, all parameters are concerning the original parameters in the relevant algorithm literature. The experimental results are shown in Tables 5 , 6 , 7 and 8 and Figs. 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 and 36 .

Evolution curve of NDWPSO and other algorithms for f1 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f2 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f3(Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f4 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f5 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f6 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f7 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f8 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f9(Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f10 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f11 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f12 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f13 (Dim = 30,50,100).

Evolution curve of NDWPSO and other algorithms for f14, f15, f16.

Evolution curve of NDWPSO and other algorithms for f17, f18, f19.

Evolution curve of NDWPSO and other algorithms for f20, f21, f22.

Evolution curve of NDWPSO and other algorithms for f23.

The experimental data of NDWPSO and other intelligent algorithms for handling 30, 50, and 100-dimensional benchmark functions ( ${f}_{1}-{f}_{13}$ ) are recorded in Tables 8 , 9 and 10 , respectively. The comparison data of fixed-multimodal benchmark tests ( ${f}_{14}-{f}_{23}$ ) are recorded in Table 11 . According to the data in Tables 5 , 6 and 7 , the NDWPSO algorithm obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function ( ${f}_{1}-{f}_{13}$ ) in the search space of three dimensions (Dim = 30, 50, 100), respectively. In Table 8 , the NDWPSO algorithm obtains 80% of the optimal solutions in 10 fixed-multimodal benchmark functions.

The convergence curves of each algorithm are shown in Figs. 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 and 36 . The NDWPSO algorithm demonstrates two convergence behaviors when calculating the benchmark functions in 30, 50, and 100-dimensional search spaces. The first behavior is the fast convergence of NDWPSO with a small number of iterations at the beginning of the search. The reason is that the Iterative-mapping strategy and the position update scheme of dynamic weighting are used in the NDWPSO algorithm. This scheme can quickly target the region in the search space where the global optimum is located, and then precisely lock the optimal solution. When NDWPSO processes the functions ${f}_{1}-{f}_{4}$ , and ${f}_{9}-{f}_{11}$ , the behavior can be reflected in the convergence trend of their corresponding curves. The second behavior is that NDWPSO gradually improves the convergence accuracy and rapidly approaches the global optimal in the middle and late stages of the iteration. The NDWPSO algorithm fails to converge quickly in the early iterations, which is possible to prevent the swarm from falling into a local optimal. The behavior can be demonstrated by the convergence trend of the curves when NDWPSO handles the functions ${f}_{6}$ , ${f}_{12}$ , and ${f}_{13}$ , and it also shows that the NDWPSO algorithm has an excellent ability of local search.

Combining the experimental data with the convergence curves, it is concluded that the NDWPSO algorithm has a faster convergence speed, so the effectiveness and global convergence of the NDWPSO algorithm are more outstanding than other intelligent algorithms.

Experiments on classical engineering problems

Three constrained classical engineering design problems (welded beam design, pressure vessel design 43 , and three-bar truss design 38 ) are used to evaluate the NDWPSO algorithm. The experiments are the NDWPSO algorithm and 5 other intelligent algorithms (WOA 36 , HHO, GWO, AOA, EO 41 ). Each algorithm is provided with the maximum number of iterations and population size ( ${\text{Mk}}=500,\mathrm{ n}=40$ ), and then repeats 30 times, independently. The parameters of the algorithms are set the same as in Table 4 . The experimental results of three engineering design problems are recorded in Tables 9 , 10 and 11 in turn. The result data is the average value of the solved data.

Welded beam design

The target of the welded beam design problem is to find the optimal manufacturing cost for the welded beam with the constraints, as shown in Fig. 37 . The constraints are the thickness of the weld seam ( ${\text{h}}$ ), the length of the clamped bar ( ${\text{l}}$ ), the height of the bar ( ${\text{t}}$ ) and the thickness of the bar ( ${\text{b}}$ ). The mathematical formulation of the optimization problem is given as follows:

Welded beam design.

In Table 9 , the NDWPSO, GWO, and EO algorithms obtain the best optimal cost. Besides, the standard deviation (SD) of t NDWPSO is the lowest, which means it has very good results in solving the welded beam design problem.

Pressure vessel design

Kannan and Kramer 43 proposed the pressure vessel design problem as shown in Fig. 38 to minimize the total cost, including the cost of material, forming, and welding. There are four design optimized objects: the thickness of the shell ${T}_{s}$ ; the thickness of the head ${T}_{h}$ ; the inner radius ${\text{R}}$ ; the length of the cylindrical section without considering the head ${\text{L}}$ . The problem includes the objective function and constraints as follows:

Pressure vessel design.

The results in Table 10 show that the NDWPSO algorithm obtains the lowest optimal cost with the same constraints and has the lowest standard deviation compared with other algorithms, which again proves the good performance of NDWPSO in terms of solution accuracy.

Three-bar truss design

This structural design problem 44 is one of the most widely-used case studies as shown in Fig. 39 . There are two main design parameters: the area of the bar1 and 3 ( ${A}_{1}={A}_{3}$ ) and area of bar 2 ( ${A}_{2}$ ). The objective is to minimize the weight of the truss. This problem is subject to several constraints as well: stress, deflection, and buckling constraints. The problem is formulated as follows:

Three-bar truss design.

From Table 11 , NDWPSO obtains the best design solution in this engineering problem and has the smallest standard deviation of the result data. In summary, the NDWPSO can reveal very competitive results compared to other intelligent algorithms.

Conclusions and future works

An improved algorithm named NDWPSO is proposed to enhance the solving speed and improve the computational accuracy at the same time. The improved NDWPSO algorithm incorporates the search ideas of other intelligent algorithms (DE, WOA). Besides, we also proposed some new hybrid strategies to adjust the distribution of algorithm parameters (such as the inertia weight parameter, the acceleration coefficients, the initialization scheme, the position updating equation, and so on).

23 classical benchmark functions: indefinite unimodal (f1-f7), indefinite multimodal (f8-f13), and fixed-dimensional multimodal(f14-f23) are applied to evaluate the effective line and feasibility of the NDWPSO algorithm. Firstly, NDWPSO is compared with PSO, CDWPSO, and SDWPSO. The simulation results can prove the exploitative, exploratory, and local optima avoidance of NDWPSO. Secondly, the NDWPSO algorithm is compared with 5 other intelligent algorithms (WOA, HHO, GWO, AOA, EO). The NDWPSO algorithm also has better performance than other intelligent algorithms. Finally, 3 classical engineering problems are applied to prove that the NDWPSO algorithm shows superior results compared to other algorithms for the constrained engineering optimization problems.

Although the proposed NDWPSO is superior in many computation aspects, there are still some limitations and further improvements are needed. The NDWPSO performs a limit initialize on each particle by the strategy of “elite opposition-based learning”, it takes more computation time before speed update. Besides, the” local optimal jump-out” strategy also brings some random process. How to reduce the random process and how to improve the limit initialize efficiency are the issues that need to be further discussed. In addition, in future work, researchers will try to apply the NDWPSO algorithm to wider fields to solve more complex and diverse optimization problems.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by Key R&D plan of Shandong Province, China (2021CXGC010207, 2023CXGC01020); First batch of talent research projects of Qilu University of Technology in 2023 (2023RCKY116); Introduction of urgently needed talent projects in Key Supported Regions of Shandong Province; Key Projects of Natural Science Foundation of Shandong Province (ZR2020ME116); the Innovation Ability Improvement Project for Technology-based Small- and Medium-sized Enterprises of Shandong Province (2022TSGC2051, 2023TSGC0024, 2023TSGC0931); National Key R&D Program of China (2019YFB1705002), LiaoNing Revitalization Talents Program (XLYC2002041) and Young Innovative Talents Introduction & Cultivation Program for Colleges and Universities of Shandong Province (Granted by Department of Education of Shandong Province, Sub-Title: Innovative Research Team of High Performance Integrated Device).

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College of Engineering Adds Research Showcase to April 26 Senior Design Expo

Tile drainage systems installed under rows of agricultural crops don’t just carry away excess subsurface water. These systems also carry away nutrients, especially when a sudden rain prevents fertilizer from properly saturating the soil.

That’s a problem for farmers, whose crops and finances stand to suffer.

UToledo engineering students work on their project for the annual Senior Design Expo.

The Senior Design Expo will be combined for the first time this semester with a new Day of Research, highlighting additional student and faculty research activities in the college.

It’s also a problem for wider communities affected by harmful algal blooms that are fed by common agricultural nutrients like phosphorus and nitrogen.

“If it rains within 24 hours of when you spray fertilizer, those chemical run straight off. They leach into the watershed,” said Ethan Altman, a mechanical engineering technology senior who can speak from experience having grown up on his family’s farm near Holgate, Ohio. “That’s a major problem, especially in northwest Ohio with the harmful algal blooms on western Lake Erie. So we’re hoping we can fix it.”

Altman is part of a team of five mechanical engineering technology seniors who share that goal ahead of the College of Engineering’s Senior Design Expo on Friday, April 26. Their working model of a multipart system designed to better manage agricultural drainage and irrigation will be one of more than 25 self-guided projects on display. Each showcases students’ creativity and ingenuity while tackling a real-world challenge, in some cases in collaboration with local businesses, industries and federal agencies.

A long-standing tradition and a requirement for graduation in the College of Engineering, the Senior Design Expo will be combined for the first time this semester with a new Day of Research, highlighting additional student and faculty research activities in the college.

“The College of Engineering is a leader on campus in research and scholarly activities,” said Dr. Patricia Relue, senior associate dean of research and graduate studies in the College of Engineering and a professor in the Department of Bioengineering. “During the event on Friday, April 26, our team will unveil several new research initiatives to enhance the position of our college in leading large-scale research projects.”

The Senior Design Expo is from noon to 3 p.m. on the first floor of Nitschke Hall. The Day of Research runs concurrently with student research poster presentations from noon to 3 p.m. on the first floor of Nitschke Hall, tours of labs from 1:30 to 2:30 p.m. in the newly renovated North Engineering Building and rapid faculty research presentations followed by a networking reception from 3 to 4 p.m. in North Engineering 2108.

Visitor parking is free between 11:30 a.m. and 5 p.m. in Area 20. For more information on the event, go to the College of Engineering website .

Altman’s insights into tile drainage informed his senior design project with Thomas Codispoti, Robbie Nicely, Luke Reese and Jacob Stokes. Tile drainage is a technique to remove subsurface water from fields dating back to the ancient Romans. Its name reflects the ceramic tiles that once made up these systems of underground tunnels, although plastic tubing is a more commonly used material today.

The student team developed a system that collects the water that’s removed from the fields via tile drainage in a pond or reservoir, where it waits until moisture sensors placed in the fields indicate crops are getting thirsty. This triggers an automated pump to push the nutrient-rich water back into the field through the same system of “tile” tubing.

Another element of their design addresses the occasional but inevitable situation where heavy rains overflow the reservoir. They propose a filter to capture nitrates and phosphates before the water enters the watershed.

Senior Design Expo guests will see a small-scale working model of their system, with a dirt-filled container representing the field and a bucket representing the reservoir.

Altman, Codispoti, Nicely, Reese and Stokes credit the semester-long experience of building their model as a positive learning opportunity. In working on the automated relationship between the moisture sensor and the pump, for example, Nicely advanced his command of the programming language C++. The whole team worked through trial and error to determine the size of the pump needed for their model.

Stokes described another challenge.

“As engineers our main goal is to problem solve, and one of our problems we had to solve was the budget,” he said. “We had to develop unique ideas, like 3D-printing the fittings on our tile system. The fittings are basically identical to the ones you can buy in a store but making them ourselves helped us reduce the cost.”

Other examples of projects to be on display at the Senior Design Expo include a continuous positive airway pressure (CPAP) machine to specifically accommodate patients in intensive care units, a billboard to enable advertisers to target passers-by through object-recognition technology and a design for a pedestrian bridge across the Ottawa River in the village of Ottawa Hills.

“The senior design project is a key example of experiential learning in the College of Engineering,” said Dr. Mohammad Elahinia, interim dean of the College of Engineering and a University Distinguished Professor in the Department of Mechanical, Industrial and Manufacturing Engineering. “Students also get practical experience from project-based learning, mandatory co-ops and graduate research opportunities.”

The irrigation system team said they see real-world potential for their model.

“I’ve talked to farmers back home about it, and they think they could use it,” Altman said. “So hopefully, one day, we’ll have a company and see this model at full scale.”

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@INPROCEEDINGS{asee_peer_45667 author = "Quintana Clark, Natasha Mallette P.E., and Jason V. Clark" title = "Integrating Cultural Sensitivity and Indigenous Knowledge in Engineering Education: Adaptive Approaches to Problem-Solving and Growth Mindset" booktitle = "2024 PNW Section Annual Meeting" year = "2024" month = "April" address = "Montana State University, Bozeman, MT" publisher = "ASEE ...
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