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Research Papers

Physical Design Tools Support and Hinder Innovative Engineering Design

[+] Author and Article Information
Jooyoung Jang

Learning Research and Development Center,  University of Pittsburgh, Pittsburgh, PA 15260joj15@pitt.edu

Christian D. Schunn

Learning Research and Development Center,  University of Pittsburgh, Pittsburgh, PA 15260schunn@pitt.edu

For a given subsystem (e.g., energy source), all ideas mentioned across team documents were collected. The full set of subsystem ideas were then sorted according to similarity by multiple experts to produce idea tress for each subsystem in which more similar ideas were more closely connected in the tree. Number of ideas explored by a given team was the number of leaf nodes examined. Breadth of ideas searched was the number of different branches explored at given height in the tree; several different levels were considered but produced similar outcomes. Each idea was also coded for four different levels of elaboration—just mentioning the idea, providing a more detailed verbal description of the idea, providing a more detailed verbal description of the idea, drawings (sketches or computer) illustrating possible instantiations of the idea, or drawings illustrating ideas functioning the design.

Box’s test for equality of covariance matrices: F(20, 422) = 1.59, p = 0.05; Levene’s test for equality of error variances: Computer, F(3, 82) = 3.00, p = 0.035, Board, F(3, 82) = 2.10, p = 0.107, Notes, F(3, 82) = 1.46, p = 0.233, Prototype, F(3, 82) = 3.99, p = 0.011. Normality assumptions were met.

J. Mech. Des 134(4), 041001 (Mar 06, 2012) (9 pages) doi:10.1115/1.4005651 History: Received July 08, 2010; Revised December 22, 2011; Published March 06, 2012; Online March 06, 2012

Engineers use various physical tools (e.g., computers, smart boards, notes, and prototypes) to support their design work. To understand cognitive processes underlying the innovative design process and to reveal the characteristics of innovation-supporting environments, we examined the pattern of tool use in 43 interdisciplinary engineering design teams enrolled in a full-semester product realization course. Teams worked all semester on a single project, with each team being assigned a different industry-sponsored project. Group meetings were video-recorded. Team success was measured in terms of meeting client requirements, and groups were divided into high, medium, and low success. Successful teams (i.e., high and medium success groups) were found to use a smart board and physical prototypes consistently more often throughout the design process, whereas unsuccessful teams (i.e., low success group) used a computer, laptop, and paper notes more often. Particularly, late adoption of physical prototypes was a key characteristic of unsuccessful teams.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

Grahic Jump Location
Figure 2

Hours of total team meeting time by team success levels

Grahic Jump Location
Figure 3

Percentage of each design tool use by the function of success group. Note that multiple tools could be used simultaneously, and thus the sum of the tools percentages typically exceeds 100%.

Grahic Jump Location
Figure 4

Percentage of each design tool use by the function of success group and phase

Grahic Jump Location
Figure 1

Schematic pathways of design tool use and outcomes

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