Prediction of the Ultimate Tensile Strength (UTS) of Asymmetric Friction Stir Welding Using Ensemble Machine Learning Methods

This research aims to develop ensemble machine-learning methods for forecasting the ultimate tensile strength (UTS) of friction stir welding (FSW). The substance utilized in the experiment was a mixture of aluminum alloys AA5083 and AA5061. An ensemble machine learning model was created to predict the UTS of the friction stir-welded seam, utilizing 11 FSW parameters as input factors and the UTS as a response variable. The proposed approach used the Gaussian process regression (GPR) and the support vector machine (SVM) model of machine learning to build the ensemble machine learning model. In addition, an efficient technique using a differential evolution algorithm to optimize the weight for the decision fusion was incorporated into the proposed model. The effectiveness of the model was evaluated using three datasets. The first and second datasets were divided into two groups, with 80% for the training dataset and 20% for the testing dataset, while the third dataset comprised the test data to validate the model’s accuracy. The computational results indicated that the proposed model provides more accurate forecasts than existing methods, such as random forest, gradient boosting, ADA boosting, and the original SVM and GPR, by 30.67, 49.18, 16.50, 48.87, and 49.33 %, respectively. In terms of prediction accuracy, the suggested technique for decision fusion surpasses unweighted average ensemble learning (UWE) by 10.32%.