Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm

Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused...

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Vydáno v:	Polymers Ročník 15; číslo 20; s. 4085
Hlavní autoři:	Feng, Jingpeng, Zhan, Lihua, Ma, Bolin, Zhou, Hao, Xiong, Bang, Guo, Jinzhan, Xia, Yunni, Hui, Shengmeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 14.10.2023 MDPI
Témata:	Adhesive bonding Algorithms Analysis Curing Experimental methods Finite element method Interfacial bonding Machine learning Mathematical models Metal bonding Metal-metal bonding Methods New technology Optimization Process parameters Research methodology Shear strength Thickness China
ISSN:	2073-4360, 2073-4360
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Abstract	Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal–metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the Xgboost machine learning (ML) algorithm. The importance ranking of process parameters on tensile–shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The Xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 μm, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the Xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS.
AbstractList	Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal–metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the Xgboost machine learning (ML) algorithm. The importance ranking of process parameters on tensile–shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The Xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 μm, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the Xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS. Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal-metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the Xgboost machine learning (ML) algorithm. The importance ranking of process parameters on tensile-shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The Xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 μm, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the Xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS.Conventionally, the optimization of bonding process parameters requires multi-parameter repetitive experiments, the processing of data, and the characterization of complex relationships between process parameters, and performance must be achieved with the help of new technologies. This work focused on improving metal-metal bonding performance by applying SLJ experiments, finite element models (FEMs), and the Xgboost machine learning (ML) algorithm. The importance ranking of process parameters on tensile-shear strength (TSS) was evaluated with the interpretation toolkit SHAP (Shapley additive explanations) and it optimized reasonable bonding process parameters. The validity of the FEM was verified using SLJ experiments. The Xgboost models with 70 runs can achieve better prediction results. According to the degree of influence, the process parameters affecting the TSS ranked from high to low are roughness, adhesive layer thickness, and lap length, and the corresponding optimized values were 0.89 μm, 0.1 mm, and 27 mm, respectively. The experimentally measured TSS values increased by 14% from the optimized process parameters via the Xgboost model. ML methods provide a more accurate and intuitive understanding of process parameters on TSS.
Audience	Academic
Author	Xiong, Bang Zhan, Lihua Zhou, Hao Feng, Jingpeng Xia, Yunni Hui, Shengmeng Ma, Bolin Guo, Jinzhan
AuthorAffiliation	1 State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China; fengjingpeng4@gmail.com (J.F.); yjs-cast@csu.edu.cn (L.Z.); 233812049@csu.edu.cn (H.Z.); xiongbang8638@163.com (B.X.); guruci@163.com (J.G.); xyn13467535590@163.com (Y.X.); 223801010@csu.edu.cn (S.H.) 2 Light Alloys Research Institute, Central South University, Changsha 410083, China
AuthorAffiliation_xml	– name: 2 Light Alloys Research Institute, Central South University, Changsha 410083, China – name: 1 State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China; fengjingpeng4@gmail.com (J.F.); yjs-cast@csu.edu.cn (L.Z.); 233812049@csu.edu.cn (H.Z.); xiongbang8638@163.com (B.X.); guruci@163.com (J.G.); xyn13467535590@163.com (Y.X.); 223801010@csu.edu.cn (S.H.)
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SubjectTerms	Adhesive bonding Algorithms Analysis Curing Experimental methods Finite element method Interfacial bonding Machine learning Mathematical models Metal bonding Metal-metal bonding Methods New technology Optimization Process parameters Research methodology Shear strength Thickness
Title	Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm
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