Efficiency of machine learning algorithms in prediction thinning ratio and vertical forming force in two-point incremental forming process of AA1050

Abstract

Machine learning (ML) has become a popular tool for prediction in many fields due to its capability to automatically learn patterns based on training datasets to predict targets accurately. The main objective of this manuscript is to evaluate the capability of these computational techniques in predicting thinning and forming force in a two-point incremental forming process (TPIF). Different machine learning (ML) algorithms have been built using a dataset from literature to select the appropriate approach in prediction output responses. This used dataset includes four input parameters: tool nose diameter, step-down increment, sheet thickness and wall angle. An optimal set of hyperparameters has been identified to improve ML models’ performance. The prediction accuracy is typically evaluated using commonly adopted error metrics for regression cases, such as the mean squared error (MSE), the mean absolute error (MAE), and the R². ML algorithms evaluated in this work were Multilayer Perceptron, Lasso, Random Forest, Support Vector Regression and Gaussian Processes. Traditional polynomial regression with various degrees was also used for comparison purposes. It is found that SVR model with linear kernel is the most appropriate regression model in those two prediction responses.