Affiliation:
1. Lanzhou University of Technology
Abstract
Abstract
The low cycle fatigue life of 316 stainless steel is a significant basis for safety assessment. Usually, many factors affect the low-cycle fatigue life of stainless steel, and the relationship between the influencing factors and fatigue life is complicated and nonlinear. Therefore, it is hard to predict fatigue life by the traditional empirical formula. Based on this, a machine learning algorithm is proposed. In this paper, based on the existing large amount of experimental data, machine learning methods are used to predict the low circumferential fatigue life of 316 stainless steel. The results show that the prediction accuracy of nu-SVR and ELM models is high and can meet engineering needs.
Publisher
Research Square Platform LLC
Reference50 articles.
1. 1. Xiaoli Yan, Xiancheng Zhang, Shantung Tu, et al. Review of creep-fatigue endurance and life prediction of 316 stainless steels[J]. International Journal of Pressure Vessels and Piping, 2015, 126: 17–28.
2. 2. Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science[J]. APL Materials, 2016, 4(5): 053208.
3. 3. Hong Pei, Changhua Hu, Xiaosheng Si, et al. A review of machine learning-based methods for predicting the remaining life of equipment[J]. Journal of Mechanical Engineering, 2019, 55(8): 1–13.
4. 4. Mathew M D, Kim D W, Ryu W S. A neural network model to predict low cycle fatigue life of nitrogen-alloyed 316L stainless steel[J]. Materials Science and Engineering: A, 2008, 474(1): 247–253.
5. 5. Chuliang Yan, Yunxiao Hao, Kege Liu. Material fatigue life prediction by BP neural network based on genetic algorithm optimization[J]. Journal of Jilin University (Engineering), 44(6): 1710–1715.