Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning
In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9625 |
| _version_ | 1797480753662001152 |
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| author | Yicheng He Kai Yang Xiaoqing Wang Haisong Huang Jiadui Chen |
| author_facet | Yicheng He Kai Yang Xiaoqing Wang Haisong Huang Jiadui Chen |
| author_sort | Yicheng He |
| collection | DOAJ |
| description | In a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, the welding quality was predicted using the CGO–MLSSVR prediction model. Finally, the particle swarm algorithm (PSO) was used to obtain the optimal welding process parameters. The experimental results show that the CGO–MLSSVR prediction model can effectively predict the positive and negative electrode nugget diameters, and tensile shear loads, with root mean square errors of 0.024, 0.039, and 5.379, respectively, which is better than similar methods. The average relative error in weld quality for the optimal welding process parameters is within 4%, and the proposed method has a good application value in the resistance spot welding of power lithium battery packs. |
| first_indexed | 2024-03-09T22:04:37Z |
| format | Article |
| id | doaj.art-8a2d7682dbda49d2b789a31c66f07f5e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:04:37Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8a2d7682dbda49d2b789a31c66f07f5e2023-11-23T19:42:49ZengMDPI AGApplied Sciences2076-34172022-09-011219962510.3390/app12199625Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine LearningYicheng He0Kai Yang1Xiaoqing Wang2Haisong Huang3Jiadui Chen4Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, ChinaIn a small sample welding test space, and to achieve online prediction and self-optimisation of process parameters for the resistance welding joint quality of power lithium battery packs, this paper proposes a welding quality prediction model. The model combines a chaos game optimisation algorithm (CGO) with the multi-output least-squares support vector regression machine (MLSSVR), and a multi-objective process parameter optimisation method based on a particle swarm algorithm. First, the MLSSVR model was constructed, and a hyperparameter optimisation strategy based on CGO was designed. Next, the welding quality was predicted using the CGO–MLSSVR prediction model. Finally, the particle swarm algorithm (PSO) was used to obtain the optimal welding process parameters. The experimental results show that the CGO–MLSSVR prediction model can effectively predict the positive and negative electrode nugget diameters, and tensile shear loads, with root mean square errors of 0.024, 0.039, and 5.379, respectively, which is better than similar methods. The average relative error in weld quality for the optimal welding process parameters is within 4%, and the proposed method has a good application value in the resistance spot welding of power lithium battery packs.https://www.mdpi.com/2076-3417/12/19/9625resistance spot weldingquality predictionprocess optimisationchaos game optimisationmulti-output least-squares support vector regressionparticle swarm algorithm |
| spellingShingle | Yicheng He Kai Yang Xiaoqing Wang Haisong Huang Jiadui Chen Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning Applied Sciences resistance spot welding quality prediction process optimisation chaos game optimisation multi-output least-squares support vector regression particle swarm algorithm |
| title | Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning |
| title_full | Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning |
| title_fullStr | Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning |
| title_full_unstemmed | Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning |
| title_short | Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning |
| title_sort | quality prediction and parameter optimisation of resistance spot welding using machine learning |
| topic | resistance spot welding quality prediction process optimisation chaos game optimisation multi-output least-squares support vector regression particle swarm algorithm |
| url | https://www.mdpi.com/2076-3417/12/19/9625 |
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