In production system for tool wear prediction using multi-sensor time series and machine learning models

In micro-machining milling, optimizing the process is a major key to reducing production costs. By monitoring tool wear and detecting when the quality of the machining deteriorates, the process could be more accurately adjusted. The present research explores four approaches that use different machine learning models to predict machining tool wear during the milling process. The study is based on a dataset created from a face milling operation on stainless steel round (AISI 303) material. The workpiece used for the machining process is conceived as a set of multiple stairs shape in brass and is milled using a 3 mm tungsten carbide tool. Three different types of sensors—acoustic emission, accelerometers and axis currents—are set-up to measure the wear of the tool. The article provides a description of the different sensors used, and the dataset collected. The performance of the tool wear prediction is evaluated using the F1-score and a customized weighted expected value. The extra-trees classifier yields the optimal F1-score result of 73% when evaluated across the five classes, indicating its superior performance in comparison to other classifiers. Based on this best model, the implementation feasibility and the potential performance of an in-production system predicting machining tool wear is evaluated.