Research over the last several years has established the effectiveness of acoustic emission (AE) based sensing methodologies for machine condition analysis and process monitoring. Acoustic emission 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 discusses some of the motivations and requirements for sensing in automated or untended machining processes as well as reviews the research on acoustic emission (AE) sensing of tool condition (wear and fracture) in machining. The background for AE generation in metal cutting and its relationship to the condition of the cutting tool for single and multiple point tools (turning and milling) is presented. Research results will be summarized relating to the sensitivity of AE signals to process changes, AE signal sensitivity to tool condition in turning and milling for wear and fracture, AE signal processing methodologies for feature extraction including time series modeling to remove influences of machining conditions on wear tracking and AE sensor fusion using neural networks for process monitoring with several sensors.

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