Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning
To predict the forming behaviour of aluminium alloy variable diameter tubes during hydroforming, a genetic algorithm-enhanced particle swarm optimisation (GA-PSO) is used to optimise a backpropagation neural network (BP-NN). A fast prediction model based on the GA-PSO-BP neural network for the hydro...
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2025-05-01
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| author | Yong Xu Xuewei Zhang Wenlong Xie Shihong Zhang Yaqiang Tian Liansheng Chen |
| author_facet | Yong Xu Xuewei Zhang Wenlong Xie Shihong Zhang Yaqiang Tian Liansheng Chen |
| author_sort | Yong Xu |
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| description | To predict the forming behaviour of aluminium alloy variable diameter tubes during hydroforming, a genetic algorithm-enhanced particle swarm optimisation (GA-PSO) is used to optimise a backpropagation neural network (BP-NN). A fast prediction model based on the GA-PSO-BP neural network for the hydroforming of aluminium alloy variable diameter tubes was established. The loading paths (internal pressure, axial feeds, and coefficient of friction) were randomly sampled using the Latin hypercube random sampling method. The minimum wall thickness, maximum wall thickness, and maximum expansion height of the formed tubes are included in the main evaluation factors of the forming results. A variety of machine learning algorithms are used to predict, and the prediction results are compared with the finite element model in terms of error. The maximum average absolute value error and mean square error of the proposed model are less than 0.2, which improves the accuracy by 20.4% compared to the unoptimised PSO-BP neural network algorithm. The maximum error between simulated and predicted results is within 4%. The model allows effective prediction of the hydroforming effect of aluminium alloy variable diameter tubes and improves the computational rate and model accuracy of the model. The same process parameters are experimentally verified, the minimum wall thickness of the formed part is 1.27 mm, the maximum wall thickness is 1.53 mm, and the maximum expansion height is 5.11 mm. The maximum thinning and the maximum thickening rate comply with the standard of hydroforming, and the tube has good formability without obvious defects. |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-adfd8d9508c94fc4ac769b60acef1fff2025-08-20T03:52:57ZengMDPI AGApplied Sciences2076-34172025-05-01159504510.3390/app15095045Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine LearningYong Xu0Xuewei Zhang1Wenlong Xie2Shihong Zhang3Yaqiang Tian4Liansheng Chen5Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, ChinaCollege of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaInstitute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, ChinaInstitute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, ChinaCollege of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaCollege of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaTo predict the forming behaviour of aluminium alloy variable diameter tubes during hydroforming, a genetic algorithm-enhanced particle swarm optimisation (GA-PSO) is used to optimise a backpropagation neural network (BP-NN). A fast prediction model based on the GA-PSO-BP neural network for the hydroforming of aluminium alloy variable diameter tubes was established. The loading paths (internal pressure, axial feeds, and coefficient of friction) were randomly sampled using the Latin hypercube random sampling method. The minimum wall thickness, maximum wall thickness, and maximum expansion height of the formed tubes are included in the main evaluation factors of the forming results. A variety of machine learning algorithms are used to predict, and the prediction results are compared with the finite element model in terms of error. The maximum average absolute value error and mean square error of the proposed model are less than 0.2, which improves the accuracy by 20.4% compared to the unoptimised PSO-BP neural network algorithm. The maximum error between simulated and predicted results is within 4%. The model allows effective prediction of the hydroforming effect of aluminium alloy variable diameter tubes and improves the computational rate and model accuracy of the model. The same process parameters are experimentally verified, the minimum wall thickness of the formed part is 1.27 mm, the maximum wall thickness is 1.53 mm, and the maximum expansion height is 5.11 mm. The maximum thinning and the maximum thickening rate comply with the standard of hydroforming, and the tube has good formability without obvious defects.https://www.mdpi.com/2076-3417/15/9/5045variable diameter tubeshydroformingmachine learningGA-PSO-BP algorithm |
| spellingShingle | Yong Xu Xuewei Zhang Wenlong Xie Shihong Zhang Yaqiang Tian Liansheng Chen Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning Applied Sciences variable diameter tubes hydroforming machine learning GA-PSO-BP algorithm |
| title | Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning |
| title_full | Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning |
| title_fullStr | Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning |
| title_full_unstemmed | Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning |
| title_short | Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning |
| title_sort | optimisation of aluminium alloy variable diameter tubes hydroforming process based on machine learning |
| topic | variable diameter tubes hydroforming machine learning GA-PSO-BP algorithm |
| url | https://www.mdpi.com/2076-3417/15/9/5045 |
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