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Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm

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-...

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Autores principales: He, Bo, Lei, Yiyu, Jiang, Mengqi, Wang, Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654928/
https://www.ncbi.nlm.nih.gov/pubmed/36363081
http://dx.doi.org/10.3390/ma15217490
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author He, Bo
Lei, Yiyu
Jiang, Mengqi
Wang, Fei
author_facet He, Bo
Lei, Yiyu
Jiang, Mengqi
Wang, Fei
author_sort He, Bo
collection PubMed
description 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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spelling pubmed-96549282022-11-15 Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm He, Bo Lei, Yiyu Jiang, Mengqi Wang, Fei Materials (Basel) Article 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. MDPI 2022-10-25 /pmc/articles/PMC9654928/ /pubmed/36363081 http://dx.doi.org/10.3390/ma15217490 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
He, Bo
Lei, Yiyu
Jiang, Mengqi
Wang, Fei
Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm
title Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm
title_full Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm
title_fullStr Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm
title_full_unstemmed Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm
title_short Optimal Design of the Gating and Riser System for Complex Casting Using an Evolutionary Algorithm
title_sort optimal design of the gating and riser system for complex casting using an evolutionary algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654928/
https://www.ncbi.nlm.nih.gov/pubmed/36363081
http://dx.doi.org/10.3390/ma15217490
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