Utilizing an intelligent machine learning strategy for predicting the tensile strength and ductility properties of nitrogen doped CoCrFeMnNi based high entropy alloys

Abstract

The integration of machine learning (ML) within the realm of artificial intelligence has unveiled remarkable potential for predicting material properties. Particularly, determining critical properties such as yield strength (YS), tensile strength (UTS), and elongation in high entropy alloys (HEAs) has been a formidable challenge due to the extensive composition possibilities. The application of ML methods offers a viable solution for determining the optimal composition and mechanical properties of HEA materials. In this study, six ensemble ML algorithms, including random forest (RF), adaboost, xgboost, gradient boosting, bagging, and extra trees are employed to predict the tensile strength and ductility of nitrogen (N) doped CoCrFeMnNi based HEAs, which includes yield strength, tensile strength, and elongation properties. To explore the correlations between different features in the dataset, both the Pearson correlation and SHAP (SHapley Additive exPlanations) coefficient methods are employed. The gradient boosting ML model, in particular, distinguished itself with remarkable precision, achieving outstanding coefficient of determination (R2) values of approximately 98.25% for YS, 97.39% for UTS, and 94.45% for elongation. Moreover, it demonstrated lower mean absolute error (MAE) values for yield strength (32.62 MPa), tensile strength (37.48 MPa), and elongation (3.40%). This pattern strongly emphasizes its superiority in providing more accurate predictions compared to the other models. Notably, the model's prediction results for all three tensile properties are closely matched with the values obtained through experimental testing. This alignment between predictions and actual results serves as a crucial validation of the model's exceptional accuracy and reliability in precisely predicting these properties. Furthermore, the scrutinizing the impact of both prominent and less influential features on the ML model's performance is analyzed. Thereby, these findings underscore the remarkable effectiveness of the gradient boosting model as a predictive tool, emphasizing its ability to provide highly accurate and reliable predictions for HEAs properties.