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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37276

Title: Enhanced condition monitoring of the machining process using wavelet packet transform
Authors: Mao, Lei
Jackson, Lisa M.
Goodall, Paul A.
West, Andrew A.
Issue Date: 2018
Publisher: © Taylor and Francis
Citation: MAO, L. ... et al., 2018. Enhanced condition monitoring of the machining process using wavelet packet transform. IN: Haugen, S. ... et al. (eds). Safety and Reliability - Safe Societies in a Changing World - Proceedings of the 28th International European Safety and Reliability Conference (ESREL 2018), Trondheim, Norway, 17-21 June 2018, pp. 1477 - 1483.
Abstract: © 2018 Taylor & Francis Group, London. Tool wear in machining processes can have a detrimental impact upon the surface finish of a machined part, increase the energy consumption during manufacture and potentially, if the tool fails completely, damage incurred may require the part to be scrapped. Monitoring of the tools condition can therefore lead to preventative steps being taken to avoid excessively worn tools being used during machining, which could cause a part becoming damaged. Several studies have been devoted to condition monitoring of the machining process, including the evaluation of cutting tool condition. However, these methods are either impractical for a production environment due to lengthy monitoring time, or require knowledge of cutting parameters (e.g. spindle speed, feed rate, material, tool) which can be difficult to obtain. In this study, we aim to investigate if tool wear can be directly identified using features extracted from the electrical power signal of the entire Computer Numerical Control (CNC) machine (three phase voltage and current) captured at 50 KHz, for different cutting parameters. Wavelet packet transform is applied to extract the feature from the raw measurement under different conditions. By analyzing the energy and entropy of reconstructed signals at different frequency sub-bands, the tool wear level can be evaluated. Results demonstrate that with the selected features, the effects due to cutting parameter variation and tool wear level change can be discriminated with good quality, which paves the way for using this technique to monitor the machining process in practical applications.
Description: This is an Open Access paper. It is published by Taylor & Francis under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (CC BY-NC-ND). Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor: The work is supported by grant EP/K014137/1 for Loughborough University from the UK Engineering and Physical Sciences Research Council (EPSRC).
Version: Published
DOI: 10.1201/9781351174664
URI: https://dspace.lboro.ac.uk/2134/37276
Publisher Link: https://doi.org/10.1201/9781351174664
ISBN: 9780815386827
Appears in Collections:Conference Papers and Presentations (Aeronautical and Automotive Engineering)

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