Generative and self-supervised ensemble modeling for multivariate tool wear monitoring

Development of an effective tool wear monitor requires maximum utilization of information from associated data, especially in machine learning based modeling. However, vastly varied annotated training data is required, which is not only expensive but impractical to obtain. In the present work, a contiguous approach of artificial data generation followed up by self-supervised pre-training before supervised model fine tuning and final stacked generalized ensembling, has been adopted to develop an effective tool wear monitor in a low data regime. Cross-validated results of proposed methodology adoption in tool wear prediction on an experimental data set of few labeled samples attained an averaged MAE of 0.035, RMSE of 0.045 and MAPE of $12.5\%$ on the best case ensemble, which was comparatively superior to a purely supervised-only trained deep model on the same data set, with an overall accuracy enhancement of over $25\%$. The proposed approach provides an effective experimental data augmentation technique while simultaneously minimizing aleatoric uncertainty and allowing for utilization of information from often ignored static cutting parameters.