Identification of parameter-dependent machine learning models for tool flank wear prediction in dry titanium machining

This study uses machine learning (ML) techniques to predict maximum flank wear on cutting tools during the turning of Ti6Al4V, a titanium alloy known for its challenging machinability. We used three models (a) support vector machines (SVM), (b) random forests (RF), and (c) neural networks (NN) to predict maximum flank wear. The machining parameters are taken as input data sets, which compromise cutting speed, feed, and machining time. All the experiments are done at a constant depth of cut to predict maximum flank wear as an output variable. The forecasted maximum flank wear was validated using experimental data to verify their precision. The SVM model outperformed the other ML models in a wider range of cutting parameters concerning tool flank wear. The SVR model is the most accurate model with an average forecasting error of 3.24%. In forecasting maximum flank wear, the RF and NN models followed, with average forecasting errors of 3.88% and 3.71%, respectively. Additionally, the SVM model's robustness was further validated by its ability to maintain stability at varying cutting speeds and feed rates. The research enhances the economics of the machining process by predicting tool flank wear and fostering sustainability in the manufacturing industry, thereby demonstrating the applicability of ML techniques.