Feature extraction-based prediction of tool wear of Inconel 718 in face turning

Tool wear is a recurring topic in the cutting field, so obtaining knowledge about the tool wear process and the capability of predicting tool wear is of special importance. Cutting processes can be optimised with predictive models that are able to forecast tool wear with a suitable level of accuracy. This research focuses on the application of some regression approaches, based on machine learning techniques, to a face-turning process for Inconel 718. To begin with, feature extraction of the cutting forces is considered, to generate regression models. Subsequently, the regression models are improved with a reduced set of features obtained by computing the feature importance. The results provide evidence that the gradient-boosting regressor allows an increment in the wear prediction accuracy and the random forest regressor has the capability of detecting relevant features that characterise the turning process. They also reveal higher accuracy in predicting tool wear under high-pressure cooling as opposed to conventional lubrication.