Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach
作者： Chang-Chun Zhou , Guo-Fu Yin , Xiao-Bing Hu
DOI: 10.1016/J.MATDES.2008.06.006
关键词: Biochemical engineering 、 Genetic algorithm 、 Selection (genetic algorithm) 、 Systems engineering 、 Engineering 、 Product design 、 Life-cycle assessment 、 Sustainable products 、 Multi-objective optimization 、 Artificial neural network 、 Material selection
摘要: Material properties and selection are very important in product design. To get more sustainable products, not only the technical economic factors, but also environmental factors should be considered. satisfy requirements, evaluation indicators of materials presented. Environmental impacts were calculated by Life Cycle Assessment method (LCA method). An integration artificial neural networks (ANN) with genetic algorithms (GAs) is proposed to optimize multi-objectives material selection. It was validated an example that system can select suitable develop products.
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