The accuracy losing phenomenon in abrasive tool condition monitoring and a noval WMMC-JDA based data-driven method considered tool stochastic surface morphology

As a critically issue in the maintaining of desired part quality and manufacturing productivity, tool condition monitoring (TCM) problem is treated by multi data-driven machine learning methods with training and testing datasets generated by stochastic segmentation. For existing methods, it is assumed that samples collected from same type tools follow identical probability distribution. Different from regular tools, abrasive tools dissatisfy this assumption because of their stochastic surface morphologies. Therefore, traditional data-driven methods loss their accuracies in abrasive tool changing cases. In data driven TCM models, the macroscopic effect of abrasive tool micro-stochasticity is discovered. For the abrasive tool frequently changing cases, an abrasive tool condition monitoring (ATCM) method based on unsupervised domain adaptation is presented to deal with changes caused by stochastic surface morphology. In feature-based transfer learning, source and target domain datasets are projected into a transferred subspace to enhance the distribution alignment similarity of two domains. A weighted maximum margin criterion (WMMC) is adopted in the domain adaptation process to make the transformed samples in same class closing but segregated from those in different classes. With unsupervised domain adaptation mechanism and multilayer perceptron (MLP), the unsupervised domain adaptation is proposed to deal with traditional model invalidation after abrasive tool changes. The proposed method is verified by the robotic belt grinding experiment results. Compared with the existing data-driven method, the weighted maximum margin criterion joint distribution adaptation (WMMC-JDA) based ATCM method still maintains the predicting effectiveness after abrasive tool changes.