Time-frequency-analysis-based minor cutting edge fracture detection during end milling

Abstract Successful application of tool condition detection during end milling can ensure high-quality parts and safeguard the machining system. This paper proposes an effective algorithm that consists of wavelet-based de-noising, discrete time-frequency analysis, FFT and second differencing for the detection of minor cutting edge fracture during end milling. The algorithm can be successfully applied to extract marked features from the feed-motor current signals to indicate the minor cutting edge fracture. Some typical experiments, the cutter run-out, entry/exit cuts and cutting parameters-variation, have been performed to confirm the robustness of the algorithm. The results show that the new approach has an excellent potential for practical and real-time application at low cost for the detection of minor cutting edge fracture during end milling.