A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data

The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on particle swarm optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO-optimized SVM (PSO-SVM)-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the duration of experiment, depth of cut, feed, type of material, etc. The second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine's improvements. Firstly, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. Secondly, the main advantages of this PSO-SVM-based model are its capacity to produce a simple, easy-to-interpret model; its ability to estimate the contributions of the input variables; and its computational efficiency. Finally, the main conclusions of this study are exposed.