Research Papers

Concurrent Optimization of Computationally Learned Stylistic Form and Functional Goals

[+] Author and Article Information
Ian Tseng

Department of Mechanical Engineering,  Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213iht@alumni.cmu.edu

Jonathan Cagan

Department of Mechanical Engineering,  Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213cagan@cmu.edu

Kenneth Kotovsky

Department of Psychology,  Carnegie Mellon University, Baker Hall 342c, Pittsburg, PA 15213kotovsky@cmu.edu

J. Mech. Des 134(11), 111006 (Oct 02, 2012) (11 pages) doi:10.1115/1.4007304 History: Received August 11, 2011; Revised June 19, 2012; Published October 02, 2012; Online October 02, 2012

Great design often results from intelligently balancing tradeoffs and leveraging of synergies between multiple product goals. While the engineering design community has numerous tools for managing the interface between functional goals in products, there are currently no formalized methods to concurrently optimize stylistic form and functional requirements. This research develops a method to coordinate seemingly disparate but highly related goals of stylistic form and functional constraints in computational design. An artificial neural network (ANN) based machine learning system was developed to model surveyed consumer judgments of stylistic form quantitatively. Coupling this quantitative model of stylistic form with a genetic algorithm (GA) enables computers to concurrently account for multiple objectives in the domains of stylistic form and more traditional functional performance evaluation within the same quantitative framework. This coupling then opens the door for computers to automatically generate products that not only work well but also convey desired styles to consumers.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 5

Generated design most beautiful (left) and least beautiful (right) for participant 18

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Figure 1

Sample matlab survey

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Figure 2

Curves in vehicle design model

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Generated design most sporty (left) and least sporty (right) for participant 18

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Figure 4

Generated design most rugged (left) and least rugged (right) for participant 18

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Generated design most fuel efficient (left) and least fuel efficient (right) for participant 18

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Average ratings for generated designs, unfiltered

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Average ratings for generated designs, with less consistent participants filtered out

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The Pareto Frontier of optimal tradeoffs between the ruggedness rating and aerodynamic performance

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Pareto optimal solutions for (a) Point A, (b) Point B, (c) Point C, and (d) Point D in Fig. 9

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(a) Most aerodynamic solution, 65 cu. ft., (b) most aerodynamic solution, 75 cu. ft., and (c) most aerodynamic solution, 85 cu. ft.

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(a) Participant 3 65 cu. ft., (b) participant 18 65 cu. ft., (c) participant 3 75 cu. ft., (d) participant 18 75 cu. ft., (e) participant 3 79.96 cu. ft., and (f) participant 18 82.59 cu. ft.



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