Enhanced condition monitoring of the machining process using wavelet packet transform

© 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....

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Main Authors:	Lei Mao, Lisa Jackson, Paul Goodall, Andrew West
Format:	Default Conference proceeding
Published:	2018
Subjects:	Other engineering not elsewhere classified untagged Engineering not elsewhere classified
Online Access:	https://hdl.handle.net/2134/37276
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id	rr-article-9222794
record_format	Figshare
spelling	rr-article-92227942018-01-01T00:00:00Z Enhanced condition monitoring of the machining process using wavelet packet transform Lei Mao (51012) Lisa Jackson (1250010) Paul Goodall (1258392) Andrew West (1259121) Other engineering not elsewhere classified untagged Engineering not elsewhere classified © 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. 2018-01-01T00:00:00Z Text Conference contribution 2134/37276 https://figshare.com/articles/conference_contribution/Enhanced_condition_monitoring_of_the_machining_process_using_wavelet_packet_transform/9222794 CC BY-NC-ND 4.0
institution	Loughborough University
collection	Figshare
topic	Other engineering not elsewhere classified untagged Engineering not elsewhere classified
spellingShingle	Other engineering not elsewhere classified untagged Engineering not elsewhere classified Lei Mao Lisa Jackson Paul Goodall Andrew West Enhanced condition monitoring of the machining process using wavelet packet transform
description	© 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.
format	Default Conference proceeding
author	Lei Mao Lisa Jackson Paul Goodall Andrew West
author_facet	Lei Mao Lisa Jackson Paul Goodall Andrew West
author_sort	Lei Mao (51012)
title	Enhanced condition monitoring of the machining process using wavelet packet transform
title_short	Enhanced condition monitoring of the machining process using wavelet packet transform
title_full	Enhanced condition monitoring of the machining process using wavelet packet transform
title_fullStr	Enhanced condition monitoring of the machining process using wavelet packet transform
title_full_unstemmed	Enhanced condition monitoring of the machining process using wavelet packet transform
title_sort	enhanced condition monitoring of the machining process using wavelet packet transform
publishDate	2018
url	https://hdl.handle.net/2134/37276
_version_	1797824153147932672

Enhanced condition monitoring of the machining process using wavelet packet transform

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