Park, JH Seo, KK 2024-01-21T08:01:51Z 2024-01-21T08:01:51Z 2021-09-03 2003-12 1226-4865 https://pubs.kist.re.kr/handle/201004/138059 In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the,design of environmentally conscious products in conceptual design phase. English KOREAN SOC MECHANICAL ENGINEERS Approximate Life Cycle Assessment of product concepts using multiple regression analysis and artificial neural networks Article 10.1007/BF02982436 1 KSME INTERNATIONAL JOURNAL, v.17, no.12, pp.1969 - 1976 KSME INTERNATIONAL JOURNAL 17 12 1969 1976 scie scopus kci ART000882648 000187220800015 Engineering, Mechanical Engineering Article approximate Life Cycle Assessment product concepts environmental impact driver product attribute multiple regression analysis artificial neural networks