Augmenting Milling Process Data for Shape Error Prediction

In order to continuously evaluate process data, automatized methods are required. This is achieved using the simulation results to determine all relevant cutting conditions. Dependencies between cutting parameters, sensor signals and cutting result are identified and quantified. However, a one-dimensional model does not predict the shape error accurately. As an alternative model, a multidimensional model based on a Support Vector Machine is trained, using process forces and simulation data. The obtained prediction accuracy is significantly higher compared to the one-dimensional model and can be used to design highly reliable cutting processes.