Document Type

Article

Publication Date

11-1-2014

Publication Title

AI EDAM (Artificial Intelligence for Engineering Design, Analysis and Manufacturing)

Volume

28

Issue Number

4

First Page

369

Last Page

384

DOI

10.1017/S0890060414000535

Keywords

Cognitive-artifact Study, Complex Systems, Conceptual Design, Physical Models, Mixed Methods

Disciplines

Mechanical Engineering

Abstract

A multistudy approach is presented that allows design thinking of complex systems to be studied by triangulating causal controlled lab findings with coded data from more complex products. A case study illustration of this approach is provided. During the conceptual design of engineering systems, designers face many cognitive challenges, including design fixation, errors in their mental models, and the sunk cost effect. These factors need to be mitigated for the generation of effective ideas. Understanding the effects of these challenges in a realistic and complex engineering system is especially difficult due to a variety of factors influencing the results. Studying the design of such systems in a controlled environment is extremely challenging because of the scale and complexity of such systems and the time needed to design the systems. Considering these challenges, a mixed-method approach is presented for studying the design thinking of complex engineering systems. This approach includes a controlled experiment with a simple system and a qualitative cognitive-artifacts study on more complex engineering systems followed by the triangulation of results. The triangulated results provide more generalizable information for complex system design thinking. This method combines the advantages of quantitative and qualitative study methods, making them more powerful while studying complex engineering systems. The proposed method is illustrated further using an illustrative study on the cognitive effects of physical models during the design of engineering systems.

Comments

This is an Accepted Manuscript of an article whose Version of Record was published by Cambridge University Press in AI EDAM (Artificial Intelligence for Engineering Design, Analysis and Manufacturing) on September 30, 2014, available online: https://doi.org/10.1017/S0890060414000535. This version is free to view and download for private research and study only. Not for re-distribution, re-sale or use in derivative works. © copyright holders.

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