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

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).

Abstract

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

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