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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2016-04-01
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Online Access: | https://doi.org/10.1177/1687814016641293 |
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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 |
work_keys_str_mv |
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