In a fully automated manufacturing system, tool condition monitoring system is essential to detect the failure in advance and minimize the manufacturing loses with the increase in productivity. To look for a reliable, simple and cheap solution, this paper proposes a new tool wear monitoring model to detect the tool wear progression and early detection of tool failure in end milling using audible sound signals. In this study, cutting tools are classified into six classes based on different flank wear ranges. A series of end milling experiments are operated with a broad range of cutting conditions for each class to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to tool wear conditions. The performance evaluation results of the proposed algorithm have shown accurate predictions in detecting tool wear conditions from the sound signals. In addition, the proposed machine learning approach has shown the fastest response rate, which provides the good solution for on-line cutting tool monitoring.

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