A brief review: acoustic emission method for tool wear monitoring during turning

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process monitoring. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing processes. This paper reviews briefly the research on AE sensing of tool wear condition in turning. The main contents included are: 1. The AE generation in metal cutting processes, AE signal classification, and AE signal correction. 2. AE signal processing with various methodologies, including time series analysis, FFT, wavelet transform, etc. Z Z, 3. Estimation of tool wear condition, including pattern classification, GMDH methodology, fuzzy classifier, neural network, and Cy sensor and data fusion. A review of AE-based tool wear monitoring in turning is an important step for improving and developing new tool wear monitoring Z, methodology. (C) 2001 Elsevier Science Ltd. All rights reserved.