Towards Material-Batch-Aware Tool Condition Monitoring

Abstract

In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.

A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

“…Different from supervised methods, unsupervised TCM systems establish model parameters only by using monitoring signals rather than training data [24]. However, there are few reports on TCM research based on unsupervised methods due to their poor nonlinear fitting ability and real-world supervising ability.…”