Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

doi:10.1007/s40436-024-00491-3

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 447-464.doi: 10.1007/s40436-024-00491-3

Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

Si-Geng Li^1,2, Qiu-Ren Chen^2,3, Li Huang^2,3, Min Chen¹, Chen-Di Wei^1,2, Zhong-Jie Yue^1,2, Ru-Xue Liu⁴, Chao Tong³, Qing Liu^2,3

1. School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, People's Republic of China;
2. Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing, 210009, People's Republic of China;
3. Materials Bigdata and Application Division, Material Academy Jitri, Suzhou, 215131, Jiangsu, People's Republic of China;
4. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China

收稿日期:2023-10-17 修回日期:2023-11-30 发布日期:2024-09-07
通讯作者: Min Chen,E-mail:Min.Chen@xjtlu.edu.cn E-mail:Min.Chen@xjtlu.edu.cn
作者简介:Si-Geng Li is a Ph.D. candidate at the School of Advanced Technology at Xi’an Jiaotong-Liverpool University, China. He received B.S. degree (2020) in Information and Computing Science and M.S. degree (2023) in Recognition and Intelligent Systems from XJTLU. His research interests include fatigue life, data mining and machine learning;
Qiu-Ren Chen is a distinguished professor of Nanjing Tech University and a technical expert of Yangtze River Delta Advanced Materials Research Institute. His main research interests include fatigue durability analysis methods, failure behavior modelling of joints and industrial simulation software development;
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;
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;
Chen-Di Wei is a Ph.D. candidate at the School of Advanced Technology at Xi’an JiaotongLiverpool University, China. She received M.S. degree (2020) in Space Science and Technology from University College London. Her research interests include adhesive-bonded joint technology and machine learning;
Zhong-Jie Yue is a Ph.D. candidate at the School of Advanced Technology at Xi’an JiaotongLiverpool University, China. He received M.S. degree (2023) from Nanjing University of Science and Technology;
Ru-Xue Liu , is a Ph.D. candidate at the School of Mechanical Engineering, Shanghai Jiao Tong University, China. He received Master degree in Vehicle Engineering from Chongqing University in 2018. The main research interest focuses on the multiscale modeling of hot forming process for aluminum alloys;
Chao Tong is a machine learning engineer, graduated in 2018 with a master’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 the director of Material Academy, Jitri 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, micro-mechanisms and mechanical behaviors of metal deformation and formation mechanisms.
基金资助:
We would like to express our sincere appreciation for the support provided by the Jiangsu Industrial Technology Research Institute and the Yangtze Delta Region Institute of Advanced Materials. This research was supported by the National Natural Science Foundation of China (Grant No. 52205377), the National Key Research and Development Program (Grant No. 2022YFB4601804), and the Key Basic Research Project of Suzhou (Grant Nos. #SJC2022029, #SJC2022031).

Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

Si-Geng Li^1,2, Qiu-Ren Chen^2,3, Li Huang^2,3, Min Chen¹, Chen-Di Wei^1,2, Zhong-Jie Yue^1,2, Ru-Xue Liu⁴, Chao Tong³, Qing Liu^2,3

1. School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, People's Republic of China;
2. Key Laboratory for Light-weight Materials, Nanjing Tech University, Nanjing, 210009, People's Republic of China;
3. Materials Bigdata and Application Division, Material Academy Jitri, Suzhou, 215131, Jiangsu, People's Republic of China;
4. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China

Received:2023-10-17 Revised:2023-11-30 Published:2024-09-07
Contact: Min Chen,E-mail:Min.Chen@xjtlu.edu.cn E-mail:Min.Chen@xjtlu.edu.cn
Supported by:
We would like to express our sincere appreciation for the support provided by the Jiangsu Industrial Technology Research Institute and the Yangtze Delta Region Institute of Advanced Materials. This research was supported by the National Natural Science Foundation of China (Grant No. 52205377), the National Key Research and Development Program (Grant No. 2022YFB4601804), and the Key Basic Research Project of Suzhou (Grant Nos. #SJC2022029, #SJC2022031).

摘要/Abstract

摘要： The stress-life curve (S-N) and low-cycle strain-life curve (E-N) are the two primary representations used to characterize the fatigue behavior of a material. These material fatigue curves are essential for structural fatigue analysis. However, conducting material fatigue tests is expensive and time-intensive. To address the challenge of data limitations on ferrous metal materials, we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S-N and E-N curves of ferrous materials. In addition, a data-augmentation framework is introduced using a conditional generative adversarial network (cGAN) to overcome data deficiencies. By incorporating the cGAN-generated data, the accuracy (R²) of the Random Forest Algorithm-trained model is improved by 0.3-0.6. It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00491-3

关键词: Fatigue life curve, Machine learning, Transfer learning, Conditional generative adversarial network (cGAN)

Abstract: The stress-life curve (S-N) and low-cycle strain-life curve (E-N) are the two primary representations used to characterize the fatigue behavior of a material. These material fatigue curves are essential for structural fatigue analysis. However, conducting material fatigue tests is expensive and time-intensive. To address the challenge of data limitations on ferrous metal materials, we propose a novel method that utilizes the Random Forest Algorithm and transfer learning to predict the S-N and E-N curves of ferrous materials. In addition, a data-augmentation framework is introduced using a conditional generative adversarial network (cGAN) to overcome data deficiencies. By incorporating the cGAN-generated data, the accuracy (R²) of the Random Forest Algorithm-trained model is improved by 0.3-0.6. It is proven that the cGAN can significantly enhance the prediction accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00491-3

Key words: Fatigue life curve, Machine learning, Transfer learning, Conditional generative adversarial network (cGAN)

Si-Geng Li, Qiu-Ren Chen, Li Huang, Min Chen, Chen-Di Wei, Zhong-Jie Yue, Ru-Xue Liu, Chao Tong, Qing Liu. Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms[J]. Advances in Manufacturing, 2024, 12(3): 447-464.

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Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

Data-driven approach to predict the fatigue properties of ferrous metal materials using the cGAN and machine-learning algorithms

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