Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning

摘要

Tool wear in machining could result in poor surface finish, excessive vibration and energy consumption. Monitoring tool wear in real-time is crucial to improve manufacturing productivity and quality. While numerous sensor-based tool wear monitoring techniques have been demonstrated in laboratory environments, few tool wear monitoring systems have been deployed in factories because it is not realistic to install some of the important sensors such as dynamometers on manufacturing machines. To address this issue, a novel audio signal processing approach is introduced. This technique does not require expensive sensors but audio sensors only. A blind source separation method is used to separate source signals from noise. An extended principal component analysis is used for dimensionality reduction. Real-time multi-channel audio signals are collected during a set of milling tests under varying cutting conditions. The experimental data are used to develop and validate a predictive model. Experimental results have shown that the predictive model is capable of classifying tool wear conditions with high accuracy.