Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm

This article combined Taguchi method and analysis of variance with the culture-based quantum-behaved particle swarm optimization to determine the optimal models of gating system for aluminium (Al) A356 sand casting part. First, the Taguchi method and analysis of variance were, respectively, applied...

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Main Authors: Wen-Jong Chen, Cai-Xuan Lin, Yan-Ting Chen, Jia-Ru Lin
Format: Article
Language:English
Published: SAGE Publishing 2016-04-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814016641293
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spelling doaj-0a9d594d03824f83ae5284f604a3670d2020-11-25T03:43:56ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402016-04-01810.1177/168781401664129310.1177_1687814016641293Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithmWen-Jong ChenCai-Xuan LinYan-Ting ChenJia-Ru LinThis article combined Taguchi method and analysis of variance with the culture-based quantum-behaved particle swarm optimization to determine the optimal models of gating system for aluminium (Al) A356 sand casting part. First, the Taguchi method and analysis of variance were, respectively, applied to establish an L 27 (3 8 ) orthogonal array and determine significant process parameters, including riser diameter, pouring temperature, pouring speed, riser position and gating diameter. Subsequently, a response surface methodology was used to construct a second-order regression model, including filling time, solidification time and oxide ratio. Finally, the culture-based quantum-behaved particle swarm optimization was used to determine the multi-objective Pareto optimal solutions and identify corresponding process conditions. The results showed that the proposed method, compared with initial casting model, enabled reducing the filling time, solidification time and oxide ratio by 68.14%, 50.56% and 20.20%, respectively. A confirmation experiment was verified to be able to effectively reduce the defect of casting and improve the casting quality.https://doi.org/10.1177/1687814016641293
collection DOAJ
language English
format Article
sources DOAJ
author Wen-Jong Chen
Cai-Xuan Lin
Yan-Ting Chen
Jia-Ru Lin
spellingShingle Wen-Jong Chen
Cai-Xuan Lin
Yan-Ting Chen
Jia-Ru Lin
Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm
Advances in Mechanical Engineering
author_facet Wen-Jong Chen
Cai-Xuan Lin
Yan-Ting Chen
Jia-Ru Lin
author_sort Wen-Jong Chen
title Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm
title_short Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm
title_full Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm
title_fullStr Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm
title_full_unstemmed Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm
title_sort optimization design of a gating system for sand casting aluminium a356 using a taguchi method and multi-objective culture-based qpso algorithm
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8140
publishDate 2016-04-01
description This article combined Taguchi method and analysis of variance with the culture-based quantum-behaved particle swarm optimization to determine the optimal models of gating system for aluminium (Al) A356 sand casting part. First, the Taguchi method and analysis of variance were, respectively, applied to establish an L 27 (3 8 ) orthogonal array and determine significant process parameters, including riser diameter, pouring temperature, pouring speed, riser position and gating diameter. Subsequently, a response surface methodology was used to construct a second-order regression model, including filling time, solidification time and oxide ratio. Finally, the culture-based quantum-behaved particle swarm optimization was used to determine the multi-objective Pareto optimal solutions and identify corresponding process conditions. The results showed that the proposed method, compared with initial casting model, enabled reducing the filling time, solidification time and oxide ratio by 68.14%, 50.56% and 20.20%, respectively. A confirmation experiment was verified to be able to effectively reduce the defect of casting and improve the casting quality.
url https://doi.org/10.1177/1687814016641293
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AT yantingchen optimizationdesignofagatingsystemforsandcastingaluminiuma356usingataguchimethodandmultiobjectiveculturebasedqpsoalgorithm
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