Automatic detection of depth of cut during end milling operation using acoustic emission sensor

Any shortfall in the required depth during milling machining can affect the dimensional accuracy of the part produced and can cause a catastrophic failure to the machine. Corrective remedies to fix the dimensions inaccuracy will increase the machining time and costs. In this work, a depth-of-cut monitoring system was proposed to detect depth of cut in real time using an acoustic emission sensor and prediction model. The characteristics of the sensor signal obtained in machining processes can be complex in terms of both nonlinearity and nonstationarity. To overcome this complexity, a regression model and an artificial neural network model were used to represent the relationship between the acoustic emission signal and the depth of cut. The model was tested under different machining cases and found to be efficient in predicting the depth of cut.