Tool Wear Monitoring Using Multi-sensor Time Series and Machine Learning

In the milling process of micro-machining, the optimization process is one of the keys to reduce production cost. By monitoring the tool wear and detecting when it is no longer acceptable, the machining process can be adjusted more accurately. This research explores four approaches using different machine learning models to predict machining tool wear during the milling process. The study is based on a dataset created with a face milling operation on
stainless steel (AISI 303) round material. The machining is divided into a number of stairs and is performed with a 3mm tungsten carbide. Three different types of sensors are used to measure the wearing process, with acoustic emission, accelerometers and axis currents. The better approach achieved a f1-score of 73% on five classes with a Extra Trees Classifier.