Concurrent optimization of computationally learned stylistic form and functional goals
By: Tseng, Ian
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Contributor(s): Cagan, Jonathan.
Publisher: New York ASME 2012Edition: Vol.134(11), Nov.Description: 1-11p.Subject(s): Mechanical EngineeringOnline resources: Click here
In:
Journal of mechanical designSummary: 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.
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School of Engineering & Technology Archieval Section | Not for loan | 2024-0750 |
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.
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