Tool condition monitoring for the chipboard drilling process using automatic, signal-based tool state evaluation

Abstract

An automatic approach to tool condition monitoring is presented, with the best solution achieving overall accuracy of 94.33% and 9 misclassification errors. In the wood industry, cutting tools need to be evaluated periodically. This is especially the case when drills are concerned; since when dulled, the resulting poor-quality product may generate loss for the manufacturing company, due to the need to discard it during quality control. Each tool can be classified either as useful or useless, and the second type should be exchanged as fast as possible. Manual evaluation of tools is time consuming, which results in production downtime. This problem requires a faster, automated, and precise solution for the work environment. In response to this issue, an ensemble algorithm was developed. Different signals were collected for the input data, including feed force, cutting torque, noise, vibrations, and acoustic emission. Based on those signals, a set of 152 initial features was generated, while after feature selection 19 of them were used by the classifiers. Different algorithms were tested and evaluated in terms of overall accuracy and number of errors. The best classifiers were used to prepare ensemble solution, which was able to classify the tools accurately, with very few errors between recognized classes.