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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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Autores principales: Feng, Jingpeng, Zhan, Lihua, Ma, Bolin, Zhou, Hao, Xiong, Bang, Guo, Jinzhan, Xia, Yunni, Hui, Shengmeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610595/
https://www.ncbi.nlm.nih.gov/pubmed/37896329
http://dx.doi.org/10.3390/polym15204085
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author Feng, Jingpeng
Zhan, Lihua
Ma, Bolin
Zhou, Hao
Xiong, Bang
Guo, Jinzhan
Xia, Yunni
Hui, Shengmeng
author_facet Feng, Jingpeng
Zhan, Lihua
Ma, Bolin
Zhou, Hao
Xiong, Bang
Guo, Jinzhan
Xia, Yunni
Hui, Shengmeng
author_sort Feng, Jingpeng
collection PubMed
description 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.
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spelling pubmed-106105952023-10-28 Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm Feng, Jingpeng Zhan, Lihua Ma, Bolin Zhou, Hao Xiong, Bang Guo, Jinzhan Xia, Yunni Hui, Shengmeng Polymers (Basel) Article 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. MDPI 2023-10-14 /pmc/articles/PMC10610595/ /pubmed/37896329 http://dx.doi.org/10.3390/polym15204085 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Feng, Jingpeng
Zhan, Lihua
Ma, Bolin
Zhou, Hao
Xiong, Bang
Guo, Jinzhan
Xia, Yunni
Hui, Shengmeng
Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm
title Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm
title_full Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm
title_fullStr Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm
title_full_unstemmed Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm
title_short Metal–Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm
title_sort metal–metal bonding process research based on xgboost machine learning algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10610595/
https://www.ncbi.nlm.nih.gov/pubmed/37896329
http://dx.doi.org/10.3390/polym15204085
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