Enhancing Design Problem Formulation Skills for Engineering Design Students

2014 ◽  
Author(s):  
Mahmoud Dinar ◽  
Jami J. Shah
Keyword(s):  
Graduate Students ◽  
Engineering Design ◽  
Design Problem ◽  
Mechanical Engineering ◽  
Problem Formulation ◽  
Assessment Methods ◽  
Web Tool ◽  
Design Problems ◽  
Course Work ◽  
Design Students

Problem formulation is an essential design skill for which assessment methods have been less commonly developed. In order to evaluate the progress of a group of graduate students in mechanical engineering design in regard with the problem formulation skill, they were asked to work on three design problems using the Problem Formulator web tool during their course work. Changes in a set of measures elicited from this data were examined in addition to sketches, simulations, and working prototypes. Inventories of requirements and issues, as well as concepts derived from morphological charts were created to assess designers’ skills and outcomes.

Optimal Designs by Means of Genetic Algorithms

2021 ◽  
pp. 344-354
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Optimal Designs by Means of Genetic Algorithms

2018 ◽  
pp. 151-161
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Evaluating the Effectiveness of Problem Formulation and Ideation Skills Learned Throughout an Engineering Design Course

2015 ◽  
Author(s):  
Mahmoud Dinar ◽  
Yong-Seok Park ◽  
Jami J. Shah
Keyword(s):  
Engineering Design ◽  
Objective Assessment ◽  
Problem Formulation ◽  
Engineering Product ◽  
Design Problems ◽  
Web Based ◽  
Final Project ◽  
Design Projects ◽  
Design Skills ◽  
Long Course

Conventional syllabi of engineering design courses either do not pay enough attention to conceptual design skills, or they lack an objective assessment of those skills to show students’ progress. During a semester-long course of advanced engineering product design, we assigned three major design projects to twenty five students. For each project we asked them to formulate the problems in the Problem Formulator web-based testbed. In addition, we collected sketches for all three design problems, feasibility analyses for the last two, and a working prototype for the final project. We report the students’ problem formulation and ideation in terms of a set of nine problem formulation characteristics and ASU’s ideation effectiveness metrics respectively. We discuss the limitations that the choice of the design problems caused, and how the progress of a class of students during a semester-long design course resulted in a convergence in sets of metrics that we have defined to characterize problem formulation and ideation. We also review the results of students of a similar course which we reported last year in order to find common trends.

A Comparison of Variety Metrics in Engineering Design

2017 ◽  
Author(s):  
Daniel Henderson ◽  
Kevin Helm ◽  
Kathryn Jablokow ◽  
Seda McKilligan ◽  
Shanna Daly ◽  
...  
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Mechanical Engineering ◽  
Engineering Students ◽  
Solution Space ◽  
New Variety ◽  
Early Stages ◽  
Correlation Analyses ◽  
Design Ideas

This paper focuses on comparing and contrasting methods for assessing the variety of a group of design ideas. Variety is an important attribute of design ideas, because it indicates the extent to which the solution space has been explored. There is a greater likelihood of successfully solving a design problem when a more diverse set of ideas is generated in the early stages of design. While there are three existing metrics for variety, it has not been established how well they correlate with each other, so it is unknown whether they provide similar assessments of variety. This uncertainty inspired our investigation of the three existing metrics and, eventually, the development of a new variety metric — all of which we compared statistically and qualitatively. In particular, 104 design ideas collected from 29 sophomore mechanical engineering students were analyzed using the existing and new variety metrics. We conducted correlation analyses to determine if the four metrics were related and to what degree. We also considered the qualitative differences among these metrics, along with where they might be used most effectively. We found varying levels of statistically significant correlations among the four metrics, indicating that they are dependent. Even so, each metric offers a unique perspective on variety and may be useful in different situations.

Incorporating Rapid Prototyping Machine in Teaching Mechanical Engineering Design

2007 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Kevin Nelson
Keyword(s):  
Rapid Prototyping ◽  
Engineering Design ◽  
Mechanical Engineering ◽  
Las Vegas ◽  
Student Feedback ◽  
Solid Models ◽  
Course Work ◽  
Hands On ◽  
Physical Objects

There is a trend toward increasing exposure of students to hands-on experience in mechanical engineering design courses as these courses are usually limited to generating calculations and drawings of mechanical designs. Students in these courses may lack the ability to visualize and create the physical objects that correspond to their calculations. This limitation may negatively affect students, especially those with limited hands-on experience. To address this issue, the Department of Mechanical Engineering, University of Nevada, Las Vegas (UNLV) started requiring students to create their design using a rapid prototyping machine as a part of the Mechanical Engineering Design Course (ME 440). Students in this course work in teams to create projects starting from abstract statements. They are required to use their calculations as a means to create solid models of the components of their designs and print them on the rapid prototyping machine. Such an approach results in a better understanding of the functionalities of components as well as fit and tolerance issues. Student feedback is used as well as future venues for improving the course.

scholarly journals DESIGN PROBLEM PERCEPTION IN ENGINEERING DESIGN TEAMS

2018 ◽  
Author(s):  
Amirali Ommi ◽  
Yong Zeng ◽  
Catharine C. Marsden
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Design Teams ◽  
Test Bed ◽  
Descriptive Model ◽  
Design Problems ◽  
Team Mental Models ◽  
Problem Perception ◽  
Design Creativity ◽  
Proposed Model

 Abstract – Engineering design is a decision making process that needs a good perception of the design problem to be solved. Design problems are usually solved in a team. Teams need the existence of a good design problem perception to create design solutions. This study provides an approach for elaborating a descriptive model to describe how the perception process works within a design team. This study is going to propose an approach for integrating a theoretical model of design creativity with team mental models, so they can be used for elaborating the descriptive model of perception in design teams. The NSERC Chair in Aerospace Design Engineering (NCADE) at Concordia University holds a capstone project which will be considered to be used as a test bed for validating proposed model through experimental analysis. Proposed experiments and further research are introduced at the end of paper.

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

Abstract Optimization of dynamic engineering systems generally requires problem formulations that account for the coupling between embodiment design and control system design simultaneously. Such formulations are commonly known as combined optimal design and control (co-design) problems, and their application to deterministic systems is well established in the literature through a variety of methods. However, an issue that has not been addressed in the co-design literature is the impact of the inherent uncertainties within a dynamic system on its integrated design solution. Accounting for these uncertainties transforms the standard, deterministic co-design problem into a stochastic one, thus requiring appropriate stochastic optimization approaches for its solution. This paper serves as the starting point for research on stochastic co-design problems by proposing and solving a novel problem formulation based on robust design optimization (RDO) principles. Specifically, a co-design method known as multidisciplinary dynamic system design optimization (MDSDO) is used as the basis for an RDO problem formulation and implementation. The robust objective and inequality constraints are computed per usual as functions of their first-order-approximated means and variances, whereas analysis-based equality constraints are evaluated deterministically at the means of the random decision variables. The proposed stochastic co-design problem formulation is then implemented for two case studies, with the results indicating the importance of the robust approach on the integrated design solutions and performance measures.

Tracking Project Health Using Completeness and Specificity of Requirements: A Case Study

2014 ◽  
Author(s):  
Shraddha Joshi ◽  
Joshua D. Summers
Keyword(s):  
Engineering Design ◽  
Design Process ◽  
Mechanical Engineering ◽  
Critical Role ◽  
Capstone Course ◽  
Design Students ◽  
Project Data

Requirements play a critical role in the design process. Much of the project time is spent eliciting the requirements. However, it is observed that students primarily only consider requirements while evaluating the concepts. This paper presents a case study conducted with senior mechanical engineering design students in a capstone course to begin to understand requirement evolution throughout a project. Data in the form of weekly requirements was collected from four teams working in parallel on the same industry sponsored project. The paper introduces the concepts of completeness and specificity that could allow the use of requirements as a tool for measuring project health. The findings from the case study reveal that the completeness and specificity of requirements increase from initial week to final week.

Problem Discovery With Many-Objective Visual Analytics

2016 ◽  
Author(s):  
Matthew Woodruff ◽  
Timothy W. Simpson
Keyword(s):  
Visual Analytics ◽  
Design Problem ◽  
Problem Formulation ◽  
Design Problems ◽  
Design Decisions ◽  
Detailed Design ◽  
Default State ◽  
State Of Affairs

Problem discovery is messy. It involves many mistakes, which may be regarded as a failure to address a design problem correctly. Mistakes, however, are inevitable, and misunderstanding the problems we are working on is the natural, default state of affairs. Only through engaging in a series of mistakes can we learn important things about our design problems. This study provides a case study in Many-Objective Visual Analytics (MOVA), as applied to the problem of problem discovery. It demonstrates the process of continually correcting and improving a problem formulation while visualizing its optimization results. This process produces a new, clearer understanding of the problem and puts the designer in a position to proceed with more-detailed design decisions.

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