Fuzzy pattern recognition of AE signals for grinding burn

Grinding burn is a common phenomenon of thermal damage that has been one of the main constraints in grinding difficult-to-machine materials. Grinding burn damages materials and degrades properties, by causing tensile residual stresses or microfractures in the workpiece surface. Numerous methods have been proposed to identify grinding burn. However, the main problems of current methods are their sensitivity and robustness. This paper describes a new method of grinding burn identification with highly sensitive acoustic emission (AE) techniques. The wavelet packet transform is used to extract features from AE signals and fuzzy pattern recognition is employed for optimising features and identifying the grinding status. Experimental results show that the accuracy of grinding burn recognition is satisfactory.