Abstract
Denoising is an essential step and plays a significant role in tool condition monitoring. In the present study, four wavelet-based denoising techniques are studied and compared, including conventional hard-thresholding, conventional soft-thresholding, generalized soft-thresholding, and soft-thresholding with Stein’s unbiased risk estimate (SURE). The results show that soft-thresholding with SURE generates the lowest mean squared error, and hence is the most appropriate denoising technique for tool-edge wear detection in high speed machining of Inconel 718.
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