Evaluation of Machine Learning Models for Predicting the Hot Deformation Flow Stress of Sintered Al-Zn-Mg Alloy

In predictingflow stress, machine learning (ML) offers significant advantages by leveragingdata-driven approaches, enhancing material design, and accurately forecasting materialperformance. Thus, the present study employs various supervised ML models, includinglinear regression (Lasso and Ridge), support vector regression (SVR), ensemble methods(random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB)), andneural networks (artificial neural network (ANN), multilayer perceptron (MLP)), topredictflow stress in the hot deformation of an Al-Zn-Mg alloy. The ML methodologyinvolves sequential steps from data extraction to cross-validation and hyperparametertuning, which is conducted using the hyperopt library. Model performance is assessedusing average absolute relative error (AARE), root-mean-squared error (RMSE), andmean squared error (MSE). The results show that ensemble methods (RF, GB, XGB) andneural networks outperform traditional regression methods, demonstrating superior predic-tive accuracy. Visualization using half-violin plots reveals the models'error ranges, withXGB consistently exhibiting the best performance. SVR, RF, GB, XGB, ANN, and MLPshowed better performance than the Arrhenius model in the context of AARE and MSEmetrics. Interestingly, SVR had a somewhat higher AARE of 1.89% and an MSE of0.251 MPa2, while XGB had the lowest AARE of 0.2% and the lowest MSE of0.011 MPa2. When ML models were evaluated using the skill score in relation to the Arrhe-nius model, XGB scored higher than the support vector machine (SVM) at 0.714, with ascore of 0.986. In contrast, Lasso and Ridge exhibited negative scores of-0.847 and-0.456, respectively.