Acoustic emission for tool condition monitoring in metal cutting

The automation and optimization of the manufacturing process play an important role in improving productivity. For this, monitoring and diagnostic systems are becoming increasingly necessary in manufacturing. This paper describes an indirect technique for monitoring cutting tool conditions. An attempt is made here to extract maximum information from acoustic emission (AE) signals acquired during machining. A statistical method, the time series modelling technique, is used to extract parameters called features representing the state of the cutting process. Autoregressive (AR) parameters, power of the AE signal and AR residual signals are studied here as features and found to be effective in tool condition monitoring. Once all these features are extracted after preliminary processing of AE signals, tool status, whether worn out or not worn out ( serviceable ), is decided on the basis of a pattern recognition technique. © 1997 Elsevier Science S.A.