Intelligent diagnostic technique of machining state for grinding

Successful grinding of a final product depends upon a large number of parameters that affect the grinding result and are strongly interlinked. It is, therefore, difficult to detect directly the generation of grinding faults such as chatter vibration and burning. In this paper, to achieve the development of an intelligent diagnostic technique for chatter vibration and burning phenomena on grinding process, acoustic emission signals were processed and signal parameters of the acoustic emission were also determined. In addition, a neural network was used as a diagnostic technique of the grinding state. A momentum coefficient, learning rate, and structure of the hidden layer were determined during the iterative learning process and the performance of the diagnostic technique was evaluated.