Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach

被引：186
|
作者：
Zhou, Chang-Chun [1 ]
Yin, Guo-Fu [1 ]
Hu, Xiao-Bing [1 ]
机构：
[1] Sichuan Univ, Sch Mfg Sci & Engn, Chengdu 610065, Sichuan Prov, Peoples R China
来源：
MATERIALS & DESIGN | 2009年 / 30卷 / 04期
关键词：
Selection for material properties; Environmental performance; DESIGN; IMPACT;
D O I：
10.1016/j.matdes.2008.06.006
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Material properties and selection are very important in product design. To get more sustainable products. not only the technical and economic factors, but also the environmental factors should be considered. To satisfy the requirements, evaluation indicators of materials are presented. Environmental impacts were Calculated by the Life Cycle Assessment method (LCA method). An integration of artificial neural networks (ANN) with genetic algorithms (GAs) is proposed to optimize the multi-objectives of material selection, it was validated by an example that the system can select suitable materials to develop sustainable products. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1209 / 1215
页数：7
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