The monitoring of micro milling tool wear conditions by wear area estimation
•Developed a novel tool wear area estimation method with morphological component analysis.•Identified the noise and background properties in micromilling tool wear image.•Investigated the rationale of wear area estimation method and its connection to traditional width estimation approach.•Verified t...
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Veröffentlicht in: | Mechanical systems and signal processing 2017-09, Vol.93, p.80-91 |
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container_title | Mechanical systems and signal processing |
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creator | Zhu, Kunpeng Yu, Xiaolong |
description | •Developed a novel tool wear area estimation method with morphological component analysis.•Identified the noise and background properties in micromilling tool wear image.•Investigated the rationale of wear area estimation method and its connection to traditional width estimation approach.•Verified the effectiveness of the approach with various experimental studies.
In micro milling, the tool wear condition is key to the geometrical and surface integrity of the product. This study proposes a novel tool wear surface area monitoring approach based on the full tool wear image, which can reflect the tool conditions better than the traditional tool wear width criteria. To meet the challenges of heavy noise, blur boundary, and mis-alignment of the captured tool wear images, this paper develops a region growing algorithm based on morphological component analysis (MCA) to solve the problems. It decomposes the original micro milling tool image into target tool images, background image and noise image. Then, the region growing algorithm is used to detect the defect and extract the wear region of the target tool image. In addition, rotation invariant features are extracted from wear region to overcome the inconsistency of wear image orientation. The experiment results show that region growing based on MCA algorithm can extract the wear region of the target tool image effectively and the extracted wear region also has good indication of tool wear conditions. It also demonstrates that the estimation of wear area can generalize the tool wear width estimation approach, and yield more accurate results than the traditional approaches. |
doi_str_mv | 10.1016/j.ymssp.2017.02.004 |
format | Article |
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In micro milling, the tool wear condition is key to the geometrical and surface integrity of the product. This study proposes a novel tool wear surface area monitoring approach based on the full tool wear image, which can reflect the tool conditions better than the traditional tool wear width criteria. To meet the challenges of heavy noise, blur boundary, and mis-alignment of the captured tool wear images, this paper develops a region growing algorithm based on morphological component analysis (MCA) to solve the problems. It decomposes the original micro milling tool image into target tool images, background image and noise image. Then, the region growing algorithm is used to detect the defect and extract the wear region of the target tool image. In addition, rotation invariant features are extracted from wear region to overcome the inconsistency of wear image orientation. The experiment results show that region growing based on MCA algorithm can extract the wear region of the target tool image effectively and the extracted wear region also has good indication of tool wear conditions. It also demonstrates that the estimation of wear area can generalize the tool wear width estimation approach, and yield more accurate results than the traditional approaches.</description><identifier>ISSN: 0888-3270</identifier><identifier>EISSN: 1096-1216</identifier><identifier>DOI: 10.1016/j.ymssp.2017.02.004</identifier><language>eng</language><publisher>Berlin: Elsevier Ltd</publisher><subject>Algorithms ; Background noise ; Electrical engineering ; Feature extraction ; Image detection ; Micro milling ; Monitoring ; Morphological component analysis ; Region growing ; Signal processing ; Target recognition ; Tool wear ; Tool wear area estimation ; Wear</subject><ispartof>Mechanical systems and signal processing, 2017-09, Vol.93, p.80-91</ispartof><rights>2017 Elsevier Ltd</rights><rights>Copyright Elsevier BV Sep 1, 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c331t-e1bb72018c08e9178dddb42df78c3b72d97cffe91986b136908a9f6f3f1e62e3</citedby><cites>FETCH-LOGICAL-c331t-e1bb72018c08e9178dddb42df78c3b72d97cffe91986b136908a9f6f3f1e62e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ymssp.2017.02.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27922,27923,45993</link.rule.ids></links><search><creatorcontrib>Zhu, Kunpeng</creatorcontrib><creatorcontrib>Yu, Xiaolong</creatorcontrib><title>The monitoring of micro milling tool wear conditions by wear area estimation</title><title>Mechanical systems and signal processing</title><description>•Developed a novel tool wear area estimation method with morphological component analysis.•Identified the noise and background properties in micromilling tool wear image.•Investigated the rationale of wear area estimation method and its connection to traditional width estimation approach.•Verified the effectiveness of the approach with various experimental studies.
In micro milling, the tool wear condition is key to the geometrical and surface integrity of the product. This study proposes a novel tool wear surface area monitoring approach based on the full tool wear image, which can reflect the tool conditions better than the traditional tool wear width criteria. To meet the challenges of heavy noise, blur boundary, and mis-alignment of the captured tool wear images, this paper develops a region growing algorithm based on morphological component analysis (MCA) to solve the problems. It decomposes the original micro milling tool image into target tool images, background image and noise image. Then, the region growing algorithm is used to detect the defect and extract the wear region of the target tool image. In addition, rotation invariant features are extracted from wear region to overcome the inconsistency of wear image orientation. The experiment results show that region growing based on MCA algorithm can extract the wear region of the target tool image effectively and the extracted wear region also has good indication of tool wear conditions. It also demonstrates that the estimation of wear area can generalize the tool wear width estimation approach, and yield more accurate results than the traditional approaches.</description><subject>Algorithms</subject><subject>Background noise</subject><subject>Electrical engineering</subject><subject>Feature extraction</subject><subject>Image detection</subject><subject>Micro milling</subject><subject>Monitoring</subject><subject>Morphological component analysis</subject><subject>Region growing</subject><subject>Signal processing</subject><subject>Target recognition</subject><subject>Tool wear</subject><subject>Tool wear area estimation</subject><subject>Wear</subject><issn>0888-3270</issn><issn>1096-1216</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9UE1LxDAQDaLguvoLvBQ8t84kJU0OHkT8ggUvew9tPjSlbdakq-y_N2s9e5mBefPmvXmEXCNUCMhv--owprSrKGBTAa0A6hOyQpC8RIr8lKxACFEy2sA5uUipBwBZA1-RzfbDFmOY_Byin96L4IrR6xhyHYbjYA5hKL5tGwsdJuNnH6ZUdIdl1EbbFjbNfmyPwCU5c-2Q7NVfX5Pt0-P24aXcvD2_PtxvSs0YzqXFrmuyVaFBWImNMMZ0NTWuEZplxMhGO5cRKXiHjEsQrXTcMYeWU8vW5GY5u4vhc5_lVR_2ccqKCmVNUUhkMm-xZSt_k1K0Tu1i9hkPCkEdU1O9-k1NHVNTQFVOLbPuFpbN_r-8jSppbydtjY9Wz8oE_y__B4Ayd5E</recordid><startdate>20170901</startdate><enddate>20170901</enddate><creator>Zhu, Kunpeng</creator><creator>Yu, Xiaolong</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20170901</creationdate><title>The monitoring of micro milling tool wear conditions by wear area estimation</title><author>Zhu, Kunpeng ; Yu, Xiaolong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c331t-e1bb72018c08e9178dddb42df78c3b72d97cffe91986b136908a9f6f3f1e62e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Background noise</topic><topic>Electrical engineering</topic><topic>Feature extraction</topic><topic>Image detection</topic><topic>Micro milling</topic><topic>Monitoring</topic><topic>Morphological component analysis</topic><topic>Region growing</topic><topic>Signal processing</topic><topic>Target recognition</topic><topic>Tool wear</topic><topic>Tool wear area estimation</topic><topic>Wear</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Kunpeng</creatorcontrib><creatorcontrib>Yu, Xiaolong</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Mechanical systems and signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Kunpeng</au><au>Yu, Xiaolong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The monitoring of micro milling tool wear conditions by wear area estimation</atitle><jtitle>Mechanical systems and signal processing</jtitle><date>2017-09-01</date><risdate>2017</risdate><volume>93</volume><spage>80</spage><epage>91</epage><pages>80-91</pages><issn>0888-3270</issn><eissn>1096-1216</eissn><abstract>•Developed a novel tool wear area estimation method with morphological component analysis.•Identified the noise and background properties in micromilling tool wear image.•Investigated the rationale of wear area estimation method and its connection to traditional width estimation approach.•Verified the effectiveness of the approach with various experimental studies.
In micro milling, the tool wear condition is key to the geometrical and surface integrity of the product. This study proposes a novel tool wear surface area monitoring approach based on the full tool wear image, which can reflect the tool conditions better than the traditional tool wear width criteria. To meet the challenges of heavy noise, blur boundary, and mis-alignment of the captured tool wear images, this paper develops a region growing algorithm based on morphological component analysis (MCA) to solve the problems. It decomposes the original micro milling tool image into target tool images, background image and noise image. Then, the region growing algorithm is used to detect the defect and extract the wear region of the target tool image. In addition, rotation invariant features are extracted from wear region to overcome the inconsistency of wear image orientation. The experiment results show that region growing based on MCA algorithm can extract the wear region of the target tool image effectively and the extracted wear region also has good indication of tool wear conditions. It also demonstrates that the estimation of wear area can generalize the tool wear width estimation approach, and yield more accurate results than the traditional approaches.</abstract><cop>Berlin</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ymssp.2017.02.004</doi><tpages>12</tpages></addata></record> |
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source | ScienceDirect Journals (5 years ago - present) |
subjects | Algorithms Background noise Electrical engineering Feature extraction Image detection Micro milling Monitoring Morphological component analysis Region growing Signal processing Target recognition Tool wear Tool wear area estimation Wear |
title | The monitoring of micro milling tool wear conditions by wear area estimation |
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