Abstract
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. 3. Estimation of tool wear condition, including pattern classification, GMDH methodology, fuzzy classifier, neural network, and 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 methodology.
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