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Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm.
He, Bo; Lei, Yiyu; Jiang, Mengqi; Wang, Fei.
Afiliação
  • He B; Research Center of High-Temperature Alloy Precision Forming, School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
  • Lei Y; Research Center of High-Temperature Alloy Precision Forming, School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
  • Jiang M; Research Center of High-Temperature Alloy Precision Forming, School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
  • Wang F; Research Center of High-Temperature Alloy Precision Forming, School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
Materials (Basel) ; 15(21)2022 Oct 25.
Article em En | MEDLINE | ID: mdl-36363081
The gating and riser system design is essential for both quality and cost in large-scale casting and is expected to reach several objectives simultaneously. However, even with the help of commercial simulation software, the design of gating and riser systems is still the result of a long-term trial-and-error optimal process owing to the conflict between the objectives. Several evolutionary algorithms (EAs) have been reported to be helpful in the selection of the geometrical dimensions of gating and riser systems. In this study, a route with sequential use of a multi-objective EA and single-objective optimization algorithm was developed to help design gating and riser systems, respectively. This route was applied in an actual case and verified using commercial simulation software. The results showed a dramatic decrease in the time cost in design and acceptable casting quality. Thus, the proposed design method is time-saving.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article