Constraint-handling techniques for generative product design systems in the mass customization context
Research output: Contribution to journal › Article
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.
|Research areas and keywords||
Subject classification (UKÄ) – MANDATORY
|Number of pages||13|
|Journal||Artificial Intelligence for Engineering Design, Analysis and Manufacturing|
|Publication status||Published - 2013|
No data available
Related research output
Axel Nordin, 2015, Division of Machine Design, Department of Design Sciences, Faculty of Engineering LTH, Lund University. 212 p.
Research output: Thesis › Doctoral Thesis (compilation)