Wavelet-Based Denoising of Vibration Signals for Tool-Edge Wear Detection in High Speed Machining of Inconel 718
Year: 2011
Type: article
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.
Source: Advanced materials research
Authors: Ning Fang, P. Pai Srinivasa, Nathan Edwards
Institution: Utah State University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1980979542