Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel
Year: 2024
Type: article
Abstract: To better predict the fatigue-related S-N curves of different series of stainless steels, 570 sets of data including fatigue test results and other performance parameters for five common types of stainless steel materials were initially collected. Eight machine learning models were deployed and analyzed using the dataset, and their predictive performances were evaluated using assessment metrics. Based on the best-performing model, corresponding S-N curves were constructed. The SHapley Additive exPlanations (SHAP) method was then applied to the optimal model to comprehensively describe and analyze the influence mechanisms of various factors on the number of cycles than stainless steel could withstand. Finally, the results predicted by the optimal model were compared with multiple design standards to verify the feasibility and effectiveness of the model in predicting the S-N curves of stainless steel materials. The results show that a GA-ANN model possesses higher prediction accuracy than other models, with a correlation coefficient R2 of 0.98 and prediction data within a twofold error margin. The feature parameter G1 constructed through feature engineering has the most significant impact on the number of cycles. The fatigue-related S-N curves predicted by the machine learning model can satisfy the requirements of design standards, demonstrating the feasibility of using this model to predict the fatigue property of stainless steel materials.
Authors: Jia Wang, Dongkui Fan, C.S. Cai
Institution: Southeast University
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,110
Awards: G1544326315, G3894890478
OpenAlex ID: W4405355703