Predicting fatigue life of automotive adhesive bonded joints: a data-driven approach using combined experimental and numerical datasets

doi:10.1007/s40436-024-00500-5

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 522-537.doi: 10.1007/s40436-024-00500-5

Predicting fatigue life of automotive adhesive bonded joints: a data-driven approach using combined experimental and numerical datasets

Chen-Di Wei^1,2, Qiu-Ren Chen^2,3, Min Chen¹, Li Huang^2,3, Zhong-Jie Yue^1,2, Si-Geng Li^1,2, Jian Wang⁴, Li Chen³, Chao Tong³, Qing Liu³

1. School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, People's Republic of China;
2. Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing, 210009, People's Republic of China;
3. Material Academy, JITRI, Suzhou, 215100, People's Republic of China;
4. Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China

收稿日期:2023-10-17 修回日期:2023-12-22 发布日期:2024-09-07
通讯作者: Qiu-Ren Chen,E-mail:chenqiu@njtech.edu.cn;Min Chen,E-mail:Min.Chen@xjtlu.edu.cn;Chen-Di Wei,E-mail:Chendi.Wei21@student.xjtlu.edu.cn E-mail:chenqiu@njtech.edu.cn;Min.Chen@xjtlu.edu.cn;Chendi.Wei21@student.xjtlu.edu.cn
作者简介:Chen-Di Wei is a Ph.D. Student at School of Advanced Technology, Xi’an JiaotongLiverpool University, China. She received a M.S. degree in Space Science and Technology from University College London. Her research interests include adhesive bonded joint technology and machine learning;
Qiu-Ren Chen is a Distinguished Professor of Nanjing Tech University, and a technical expert of JITRI Academy. His main research interests include fatigue durability analysis methods, failure behavior modeling of joints and industrial simulation software development;
Min Chen is a Senior Associate Professor at the School of Advanced Technology, Xi’an Jiaotong-Liverpool University. Her research primarily focuses on the multiphysics analysis of structures, multiscale design and analysis of composites, as well as reliability analysis;
Li Huang is a Vice Director of Materials Data Department at Materials Academy JITRI and Guest Professor at Nanjing Tech University. His research interests include AI algorithms & engineering applications (AI for engineering), integrated computational materials engineering (ICME), digital design of material manufacturing and joining process, and material fatigue & fracture;
Zhong-Jie Yue is a graduate from the Vehicle Engineering program at the School of Mechanical Engineering, Nanjing University of Science and Technology, currently pursuing a first-year Ph.D. at Xi’an Jiaotong-Liverpool University. His main research interests lie in advanced joining technologies and intelligent manufacturing;
Si-Geng Li is a Ph.D. student in the School of Advanced Technology at Xi’an Jiaotong-Liverpool University, China. He received a B.S. degree (2020) in Information and Computing Science and a M.S. degree (2023) in Recognition and Intelligent Systems from XJTLU. His research interests include fatigue life, data mining and machine learning;
Jian Wang is a master student at Nanjing University of Science and Technology. His research interests focus on the fatigue life prediction of adhesive-selfpiercing rivet hybrid joints;
Li Chen is a CAE engineer, Advanced Materials Research Institute, Yangtze Delta. He obtained a B.S. degree from Jiangsu University of Science and Technology. He has extensive experience in conducting strength and fatigue durability analysis for vehicle structures and fatigues of tware development;
Chao Tong is a machine learning engineer. He graduated in 2018 with a M.S. degree in Material Science and Engineering from Soochow University. He is currently employed at the Materials Bigdata and Applications Division, Delta Advanced Materials Research Institute, Suzhou;
Qing Liu is Director of Material Academy, JITRI, Suzhou and the Director of the Lightweight Materials Center at Nanjing Tech University. His research interests include electron microscopy techniques for characterizing the microstructure of deformed metals, micromechanisms and mechanical behaviors of metal deformation, formation mechanisms.
基金资助:
This research has been funded by the Construction Project of the National Natural Science Foundation (Grant No. 52205377), National Key Research and Development Program (Grant No. 2022YFB4601804) and Key Basic Research Project of Suzhou (Grant Nos. #SJC2022029, #SJC2022031).

Predicting fatigue life of automotive adhesive bonded joints: a data-driven approach using combined experimental and numerical datasets

Chen-Di Wei^1,2, Qiu-Ren Chen^2,3, Min Chen¹, Li Huang^2,3, Zhong-Jie Yue^1,2, Si-Geng Li^1,2, Jian Wang⁴, Li Chen³, Chao Tong³, Qing Liu³

1. School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, People's Republic of China;
2. Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing, 210009, People's Republic of China;
3. Material Academy, JITRI, Suzhou, 215100, People's Republic of China;
4. Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China

Received:2023-10-17 Revised:2023-12-22 Published:2024-09-07
Contact: Qiu-Ren Chen,E-mail:chenqiu@njtech.edu.cn;Min Chen,E-mail:Min.Chen@xjtlu.edu.cn;Chen-Di Wei,E-mail:Chendi.Wei21@student.xjtlu.edu.cn E-mail:chenqiu@njtech.edu.cn;Min.Chen@xjtlu.edu.cn;Chendi.Wei21@student.xjtlu.edu.cn
Supported by:
This research has been funded by the Construction Project of the National Natural Science Foundation (Grant No. 52205377), National Key Research and Development Program (Grant No. 2022YFB4601804) and Key Basic Research Project of Suzhou (Grant Nos. #SJC2022029, #SJC2022031).

摘要/Abstract

摘要： The majority of vehicle structural failures originate from joint areas. Cyclic loading is one of the primary factors in joint failures, making the fatigue performance of joints a critical consideration in vehicle structure design. The use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint geometries. Therefore, there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive industry. In this work, we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis (FEA) results. Different machine learning (ML) algorithms are adopted to investigate the fatigue life of three typical adhesive joints, namely lap shear, coach peel and KSII joints. After the feature engineering and tuned process of the ML models, the preferable model using the Gaussian process regression algorithm is established, fed with eight input parameters, namely thicknesses of the substrates, line forces and bending moments of the adhesive bonded joints obtained from FEA. The proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased evaluation. It demonstrates that for life prediction of adhesive joints, the data-driven solutions can constitute an improvement over conventional solutions.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00500-5

关键词: Fatigue life, Adhesive bonded joints, Finite element analysis (FEA), Machine learning (ML)

Abstract: The majority of vehicle structural failures originate from joint areas. Cyclic loading is one of the primary factors in joint failures, making the fatigue performance of joints a critical consideration in vehicle structure design. The use of traditional fatigue analysis methods is constrained by the absence of adhesive life data and the wide variety of joint geometries. Therefore, there is a pressing need for an accurate fatigue life estimation method for the joints in the automotive industry. In this work, we proposed a data-driven approach embedding physical knowledge-guided parameters based on experimental data and finite element analysis (FEA) results. Different machine learning (ML) algorithms are adopted to investigate the fatigue life of three typical adhesive joints, namely lap shear, coach peel and KSII joints. After the feature engineering and tuned process of the ML models, the preferable model using the Gaussian process regression algorithm is established, fed with eight input parameters, namely thicknesses of the substrates, line forces and bending moments of the adhesive bonded joints obtained from FEA. The proposed method is validated with the test data set and part-level physical tests with complex loading states for an unbiased evaluation. It demonstrates that for life prediction of adhesive joints, the data-driven solutions can constitute an improvement over conventional solutions.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00500-5

Key words: Fatigue life, Adhesive bonded joints, Finite element analysis (FEA), Machine learning (ML)

Chen-Di Wei, Qiu-Ren Chen, Min Chen, Li Huang, Zhong-Jie Yue, Si-Geng Li, Jian Wang, Li Chen, Chao Tong, Qing Liu. Predicting fatigue life of automotive adhesive bonded joints: a data-driven approach using combined experimental and numerical datasets[J]. Advances in Manufacturing, 2024, 12(3): 522-537.

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Predicting fatigue life of automotive adhesive bonded joints: a data-driven approach using combined experimental and numerical datasets

Predicting fatigue life of automotive adhesive bonded joints: a data-driven approach using combined experimental and numerical datasets

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