The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology design. An overview of the genetic algorithm will first describe the genetics-based representations and operators used in a typical genetic algorithm search. Then, a review of previous research in structural optimization is provided. A discretized design representation, and methods for mapping genetic algorithm “chromosomes” into this representation, is then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of cantilevered plate topologies, and we investigate methods for optimizing finely-discretized design domains. The genetic algorithm’s ability to find families of highly-fit designs is also examined. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.
The genetic algorithm (GA), an optimization technique based on the theory of natural selection, is applied to structural topology design problems. After reviewing the GA and previous research in structural topology optimization, we describe a binary material/void design representation that is encoded in GA chromosome data structures. This representation is intended to approximate a material continuum as opposed to discrete truss structures. Four examples, showing the broad utility of the approach and representation, are then presented. A ®fth example suggests an alternate representation that allows continuously-variable material density. Concluding discussion suggests recommended uses of the technique and describes ongoing and possible future work. Ó 2000 Elsevier Science S.A. All rights reserved.
The genetic algorithm (GA), an optimization technique based on the theory of natural selection, is applied to structural topology design problems. After reviewing the genetic algorithm and previous research in structural topology optimization, we detail the chromosome-to-design representation which enables the genetic algorithm to perform structural topology optimization. Extending our prior investigations, this article first compares our genetic-algorithm-based technique with homogenization methods in the minimization of a structure’s compliance subject to a maximum volume constraint. We then use our technique to generate topologies combining high structural performance with a variety of material connectivity characteristics which arise directly from our discretized design representation. After discussing our findings, we describe potential future work.
Design for manufacturing (DFM) has been promoted as a way to enhance product development and production system performance. Current DFM practices encourage the minimization of the number of parts in a design through the physical integration of several geometric features in the same part. While this part integration often reduces the manufacturing cost of the product, it also can extend product development lead time, because complex parts typically require tooling with large lead times. This paper presents an economic model that makes explicit the trade-off between lower unit costs and longer product development time. This model is applied to a particular example in a field study of the application of DFM to Polaroid cameras.product design, product development, design-for-manufacturing, design-for-assembly, lead time, product cost, cost modeling
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