Tool Wear Detection Using Lipschitz Exponent and Harmonic Wavelet

The paper researches a novel engineering application of Lipschitz exponent function and harmonic wavelet for detecting tool condition. Tool wear affects often the quality grade of products and is gradually formed during cutting process. Meanwhile, since cutting noise is very strong, we think tool wear belongs to detecting weak singularity signals in strong noise. It is difficult to obtain a reliable worn result by raw sampled data. We propose singularity analysis with harmonic wavelet for data processing and a new concept of Lipschitz exponent function. The method can be quantitative tool condition and make maintaining decision. Test result was validated with 27 kinds of cutting conditions with the sharp tool and the worn tool; 54 group data are sampled by acoustic emission (AE).