首页|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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  • 万方数据
  • 维普
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 mate-rial fatigue curves are essential for structural fatigue analy-sis.However,conducting material fatigue tests is expensive and time-intensive.To address the challenge of data limita-tions 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 mate-rials.In addition,a data-augmentation framework is intro-duced 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 predic-tion accuracy of the machine-learning model and balance the cost of obtaining fatigue data from the experiment.

Fatigue life curveMachine learningTransfer learningConditional 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

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School of Advanced Technology,Xi'an Jiaotong-Liverpool University,Suzhou 215123,Jiangsu,People's Republic of China

Key Laboratory for Light-weight Materials,Nanjing Tech University,Nanjing 210009,People's Republic of China

Materials Bigdata and Application Division,Material Academy Jitri,Suzhou 215131,Jiangsu,People's Republic of China

School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai 200240,People's Republic of China

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2024

10.1007/s40436-024-00491-3

先进制造进展(英文版)

先进制造进展(英文版)

ISSN：
年,卷(期)：2024.12(3)