Comparison of cutting tool wear classification performance with artificial intelligence techniques

Abstract. Optimal replacement of machining cutting tools is a major challenge in today's manufacturing industry. Due to the degradation of the tool during machining, late replacement of the tool leads to the risk of producing parts that do not meet technical specifications, while early replacement increases machine downtime and tool costs. To replace tools at the right time, it is necessary to monitor their degradation. Therefore, this paper compares the classification performance of different artificial intelligence approaches to classify the condition of cutting tools from cutting signals. Different approaches, namely: Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF) and k-Nearest Neighbour (k-NN) are tested, and their performance is compared. It is highlighted that ANN and RF methods obtain better classification performances (88.8% and 86.4%, respectively) than the rest of the approaches (80%). Nevertheless, all approaches can monitor the degradation of cutting tools in a satisfactory manner (i.e., 80% accuracy). A comparison of training times highlights that training a neural network takes longer than the other approaches. However, with the computational power currently available, this is not an obstacle for their implementation in real applications as this training can still be achieved in a couple of minutes.