Tool life predictions in milling using spindle power with the neural network technique

Tool wear is an important limitation to machining productivity and part quality. In this paper, remaining useful life (RUL) prediction of tools is demonstrated based on the machine spindle power values using the neural network (NN) technique. End milling tests were performed on a stainless steel workpiece at different spindle speeds and spindle power was recorded. The NN curve fitting approach with different MATLAB (TM) training functions was applied to the root mean square power (P-rms) values. Sample P-rms growth curves were generated to take into account uncertainty. The P-rms value in the time domain was found to be sensitive to tool wear. Results show a good agreement between the predicted and true RUL of tools. The proposed method takes into account the uncertainty in tool life and the percentage increase in nominal P-rms value during the RUL prediction. Using MATLAB (TM) on an Intel i7 processor, the computation takes 0.5 s Thus, the method is computationally inexpensive and can be incorporated for real time RUL predictions during machining. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.