Estimating the S-N Curve by Machine Learning Random Forest Method

Abstract

Fatigue limit is well predicted by tensile strength or hardness, and the relationship is often analyzed by linear regression using the minimum squared approximation. However, the prediction of the number of cycles to failure at a given stress amplitude, meaning the estimate of the S–N curve, has not been realized. Therefore, we aim to investigate the estimability of the S–N curve using the random forest method based on the data described in the NIMS fatigue data sheet. The random forest method is a machine learning algorithm and an ensemble learning algorithm that integrates weak learners of multiple decision tree models to improve generalization ability. It was clarified that the machine learning of the multiple decision tree model is excellent in fatigue limit prediction. The S–N curve can be accurately estimated by combining the prediction of fatigue limit and the number of cycles to failure at a given stress amplitude.

 

This Paper was Originally Published in Japanese in J. Soc. Mater. Sci., Japan 70 (2021) 876–880.

Fig. 9 Prediction of S-N curve of fracture life using data of S25C, S35C, S55C, SNCM439, SmN438, SmN43, SUS403, SUS304 (data of fracture life of 5 × 10⁶ times or less, fatigue limit considers only hardness).