Constraint-handling techniques for generative product design systems in the mass customization context

Research output: Contribution to journalArticle

Abstract

Generative product design systems used in the context of mass customization are required to generate diverse solutions quickly and reliably without necessitating modification or tuning during use. When such systems are employed to allow for the mass customization of product form, they must be able to handle mass production and engineering constraints that can be time-consuming to evaluate and difficult to fulfill. These issues are related to how the constraints are handled in the generative design system. This article evaluates two promising sequential constraint-handling techniques and the often used weighted sum technique with regard to convergence time, convergence rate, and diversity of the design solutions. The application used for this purpose was a design system aimed at generating a table with an advanced form: a Voronoi diagram based structure. The design problem was constrained in terms of production as well as stability, requiring a timeconsuming finite element evaluation. Regarding convergence time and rate, one of the sequential constraint-handling techniques performed significantly better than the weighted sum technique. Nevertheless, the weighted sum technique presented respectable results and therefore remains a relevant technique. Regarding diversity, none of the techniques could generate diverse solutions in a single search run. In contrast, the solutions from different searches were always diverse. Solution diversity is thus gained at the cost of more runs, but no evaluation of the diversity of the solutions is needed. This result is important, because a diversity evaluation function would otherwise have to be developed for every new type of design. Efficient handling of complex constraints is an important step toward mass customization of nontrivial product forms.

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Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Production Engineering, Human Work Science and Ergonomics

Keywords

  • Genetic Algorithms, Generative Design, Evolutionary Computing, Complex Morphologies, Constraint-Handling Techniques, Renaissance 2.0, Industrial design, Industridesign, Machine design, Maskinkonstruktion
Original languageEnglish
Pages (from-to)387-399
Number of pages13
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing
Volume27
Issue number4
Publication statusPublished - 2013
Publication categoryResearch
Peer-reviewedYes

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Related research output

Axel Nordin, 2015, Division of Machine Design, Department of Design Sciences, Faculty of Engineering LTH, Lund University. 212 p.

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