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A Deep Neural Network Method for LCF Life Prediction of Metal Materials with Small Sample Experimental Data

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Abstract

Compared to traditional methods, artificial neural networks can achieve low-cycle fatigue life more accurately when considering the effects of processes and environments on metal materials. However, extensive sample data are essential for training artificial neural networks. To address the sample shortage, this paper presents a deep neural network method. First, the small samples are divided into training and test samples. Second, the training samples are divided according to the processes. Then, the new samples are generated equally based on the division using beta-variational autoencoders. Finally, the ensemble learning model is used to predict the low-cycle fatigue life of metal materials using new samples. Min–Max normalization and log10 are used to standardize and destandardize samples in the deep neural network method. The deep neural network method is evaluated using the experimental data of Ti-685, Ti-6242S, Alloy D9, and AISI 4140 steel. Furthermore, the results reveal that the deep neural network method has good predictive performance.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No.52065062), Fundamental Research Funds for Universities in Xinjiang Uygur Autonomous Region (Grant No.XJEDU2023P007), Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No.2020D01C056), and Key Research and Development Program of Xinjiang Uygur Autonomous Region (Grant No.2021B01003).

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  1. School of Mechanical Engineering, Xinjiang University, Ürümqi, 830046, China

    Haojin Yang, Jianxiong Gao, Fei Heng, Qin Cheng & Yuanyuan Liu

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Yang, H., Gao, J., Heng, F. et al. A Deep Neural Network Method for LCF Life Prediction of Metal Materials with Small Sample Experimental Data. Met. Mater. Int. (2024). https://doi.org/10.1007/s12540-023-01601-9

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