Data-Driven Usage Profiling and Anomaly Detection in Support of Sustainable Machining Processes

Abstract

There is a lot of room for improvement towards more sustainability in manufacturing companies. During the machining operations, replacement of the cutting tools is not done in an optimal way, resulting in sub-optimal usage of resources and inefficiencies during the production process. Using data-driven approaches to extend the usage of tools can greatly improve on this shortcoming by optimizing the replacement process of these tools. This study is therefore sought to investigate the value of several data-driven approaches, applied to an industrial dataset, to achieve this goal. Although the examined data-driven methods were applied to a dataset which has been generated under a wide variety of machining conditions and lacks reliable ground truth, the obtained experimental results confirm that these methods are indeed capable of extracting informative profiles from the tool usages and can identify anomalous patterns and signs in the time-series datasets collected during different machining processes.