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Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights

Tail-welded blanks (TWBs) are widely used in automotive bodies to improve the structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiment...

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Detalles Bibliográficos
Autores principales: Chen, Hao, Lu, Chihua, Liu, Zhien, Shen, Cunrui, Sun, Menglei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369873/
https://www.ncbi.nlm.nih.gov/pubmed/35955274
http://dx.doi.org/10.3390/ma15155339
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author Chen, Hao
Lu, Chihua
Liu, Zhien
Shen, Cunrui
Sun, Menglei
author_facet Chen, Hao
Lu, Chihua
Liu, Zhien
Shen, Cunrui
Sun, Menglei
author_sort Chen, Hao
collection PubMed
description Tail-welded blanks (TWBs) are widely used in automotive bodies to improve the structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (3(12)) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method, and the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs.
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spelling pubmed-93698732022-08-12 Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights Chen, Hao Lu, Chihua Liu, Zhien Shen, Cunrui Sun, Menglei Materials (Basel) Article Tail-welded blanks (TWBs) are widely used in automotive bodies to improve the structural performance and reduce weight. The stiffness and modal lightweight design optimisation of TWBs for automotive doors was performed in this study. The finite element model was validated through physical experiments. An L27 (3(12)) Taguchi orthogonal array was used to collect the sample points. The multi-objective optimisation problem was transformed into a single-objective optimisation problem based on the grey relational degree. The optimal combination of structural design parameters was obtained for a tail-welded door using the proposed method, and the weight of the door structure was reduced by 2.83 kg. The proposed optimisation method has fewer iterations and a lower computational cost, enabling the design of lightweight TWBs. MDPI 2022-08-03 /pmc/articles/PMC9369873/ /pubmed/35955274 http://dx.doi.org/10.3390/ma15155339 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
Chen, Hao
Lu, Chihua
Liu, Zhien
Shen, Cunrui
Sun, Menglei
Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights
title Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights
title_full Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights
title_fullStr Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights
title_full_unstemmed Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights
title_short Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights
title_sort multi-response optimisation of automotive door using grey relational analysis with entropy weights
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369873/
https://www.ncbi.nlm.nih.gov/pubmed/35955274
http://dx.doi.org/10.3390/ma15155339
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