Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy

As industrial development drives the increasing demand for steel, accurate estimation of the material's fatigue strength has become crucial. Fatigue strength, a critical mechanical property of steel, is a primary factor in component failure within engineering applications. Traditional fatigue testing is both costly and time-consuming, and fatigue failure can lead to severe consequences. Therefore, the need to develop faster and more efficient methods for predicting fatigue strength is evident. In this paper, a fatigue strength dataset was established, incorporating data on material element composition, physical properties, and mechanical performance parameters that influence fatigue strength. A machine learning regression model was then applied to facilitate rapid and efficient fatigue strength prediction of ferrous alloys. Twenty characteristic parameters, selected for their practical relevance in engineering applications, were used as input variables, with fatigue strength as the output. Multiple algorithms were trained on the dataset, and a deep learning regression model was employed for the prediction of fatigue strength. The performance of the models was evaluated using metrics such as MAE, RMSE, R2, and MAPE. The results demonstrated the superiority of the proposed models and the effectiveness of the applied methodologies.