A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys

Fatigue is a prevalent failure mode in mechanical structures, and the significance of fatigue strength as a process parameter for predicting the life of a material or structure cannot be emphasized enough. Given the high cost and time consumption associated with traditional experimental approaches for determining fatigue strength, which leads to prolonged analysis and design cycles, it is crucial to investigate efficient and precise methods for predicting fatigue strength. In this paper, we present a fatigue strength prediction method that utilizes Bayesian optimization for the random forest regression (RFR) model. During the training of the RFR model, a Bayesian optimization algorithm is employed to optimize the five main parameters of the model, mitigating the issue of overfitting or underfitting. The optimized RFR model demonstrates superior performance and accuracy in comparison to linear regression (LR), artificial neural network (ANN), decision tree regression (DTR), and support vector regression (SVR). This is determined by evaluating the mean absolute error (MAE), root mean square error (RMSE), Rsquared (R2), and mean absolute percentage error (MAPE) scores of the feature parameter-fatigue strength dataset. The fatigue strength of unknown ferrous alloys can be predicted with the proposed model, which largely reduces the need to obtain fatigue strength data through traditional fatigue tests and saves fatigue analysis time and test costs.