Technical Briefs

A Study of Student Design Team Behaviors in Complex System Design

[+] Author and Article Information
Jesse Austin-Breneman

e-mail: jlab@mit.edu

Tomonori Honda

e-mail: tomonori@mit.edu
Department of Mechanical Engineering,
Massachusetts Institute of Technology,
Cambridge, MA 02139

Maria C. Yang

Department of Mechanical Engineering and
Engineering Systems Division,
Massachusetts Institute of Technology,
Cambridge, MA 02139
e-mail: mcyang@mit.edu

Contributed by the Design Education Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 12, 2011; final manuscript received August 10, 2012; published online November 15, 2012. Assoc. Editor: Janis Terpenny.

J. Mech. Des 134(12), 124504 (Nov 15, 2012) (7 pages) doi:10.1115/1.4007840 History: Received July 12, 2011; Revised August 10, 2012

Large-scale engineering systems require design teams to balance complex sets of considerations using a wide range of design and decision-making skills. Formal, computational approaches for optimizing complex systems offer strategies for arriving at optimal solutions in situations where system integration and design optimization are well-formulated. However, observation of design practice suggests engineers may be poorly prepared for this type of design. Four graduate student teams completed a distributed, complex system design task. Analysis of the teams' design histories suggests three categories of suboptimal approaches: global rather than local searches, optimizing individual design parameters separately, and sequential rather than concurrent optimization strategies. Teams focused strongly on individual subsystems rather than system-level optimization, and did not use the provided system gradient indicator to understand how changes in individual subsystems impacted the overall system. This suggests the need for curriculum to teach engineering students how to appropriately integrate systems as a whole.

Copyright © 2012 by ASME
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Fig. 3

Comparison of four best design results selected by the teams. The Pareto frontier serves as a baseline. The percentage next to each point is the compatibility error of that solution.

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Fig. 4

Design history for each team. Each path shows the design points explored by individual teams.

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Fig. 5

Compatibility Error between subsystems as function of design iteration

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Fig. 1

Input and output variables for satellite design example

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Fig. 2

Modified game theoretic team structure for the design team and task. The three subsystems are allowed to confer and negotiate directly without a mediator.




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