A Robust Tool Condition Monitoring System Based on Cluster Density under Variable Machining Processes

Abstract

Real-time tool condition monitoring (TCM) is becoming more and more important to meet the increased requirement of reducing downtime and ensuring the machining quality of manufacturing systems. However, it is difficult to satisfy both robustness and effectiveness of pattern recognition for a TCM system without using an unsupervised strategy. In this paper, a clustering-based TCM system is proposed that can be used for different machining conditions such as variable cutting parameters, variable cutters, and even variable cutting methods. The solution is based on a significant statistical correlation between tool wear and the distribution of cutting force features, which is revealed through the clustering results obtained from a novel clustering method based on adjacent grids searching (CAGS). This statistical correlation is converted into tool wear status by using an empirical factor that is robust for variable cutting processes. The proposed TCM system is completely unsupervised as a training-free procedure is used in the monitoring process. To verify the effectiveness of the system, a series of experiments are conducted, such as whole life-cycle wear experiment under same milling condition, tool wear experiment under variable milling conditions and tool wear experiment under same turning condition. The prediction accuracy of our system for tool wear experiment under variable milling conditions is 100%, 75% and 75%, respectively. In contrast, BP neural network, Bayesian network and SVM are used for tool wear prediction under the same conditions. Experimental results show the superiority and effectiveness of our TCM system based on cluster density of CAGS over several state-of-the-art supervised methods.