Chatter detection in milling based on singular spectrum analysis
Chatter is a frequently encountered problem in metal cutting field which reduces the machining efficiency and surface quality. Therefore, a reliable and robust chatter detection method is necessary to improve the machining performances. In this work, a novel milling chatter detection approach based...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2018-04, Vol.95 (9-12), p.3475-3486 |
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| container_title | International journal of advanced manufacturing technology |
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| creator | Mei, Yonggang Mo, Rong Sun, Huibin Bu, Kun |
| description | Chatter is a frequently encountered problem in metal cutting field which reduces the machining efficiency and surface quality. Therefore, a reliable and robust chatter detection method is necessary to improve the machining performances. In this work, a novel milling chatter detection approach based on singular spectrum analysis (SSA) is proposed. SSA is applied to process the cutting force signal and extract the feature that is closely related to the machining state. The singular value spectrum obtained by SSA is used to describe the energy distribution of the principal modes in the signal. On the basis of frequency domain chatter theory, singular value entropy (SVE) is adopted to evaluate the variation of energy distribution in the signal and the milling chatter is detected accordingly. Milling experiments under different cutting conditions are performed out to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can accurately identify the onset of chatter. This method is simple in operation and fast in calculation, which makes it have great potential for online chatter detection. |
| doi_str_mv | 10.1007/s00170-017-1366-y |
| format | Article |
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Therefore, a reliable and robust chatter detection method is necessary to improve the machining performances. In this work, a novel milling chatter detection approach based on singular spectrum analysis (SSA) is proposed. SSA is applied to process the cutting force signal and extract the feature that is closely related to the machining state. The singular value spectrum obtained by SSA is used to describe the energy distribution of the principal modes in the signal. On the basis of frequency domain chatter theory, singular value entropy (SVE) is adopted to evaluate the variation of energy distribution in the signal and the milling chatter is detected accordingly. Milling experiments under different cutting conditions are performed out to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can accurately identify the onset of chatter. This method is simple in operation and fast in calculation, which makes it have great potential for online chatter detection.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-017-1366-y</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>CAE) and Design ; Chatter ; Computer-Aided Engineering (CAD ; Cutting force ; Cutting parameters ; Energy distribution ; Engineering ; Feature extraction ; Industrial and Production Engineering ; Mechanical Engineering ; Media Management ; Metal cutting ; Milling (machining) ; Original Article ; Signal processing ; Spectrum analysis ; Surface properties ; Vibration</subject><ispartof>International journal of advanced manufacturing technology, 2018-04, Vol.95 (9-12), p.3475-3486</ispartof><rights>Springer-Verlag London Ltd., part of Springer Nature 2017</rights><rights>Copyright Springer Science & Business Media 2018</rights><rights>The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2017). All Rights Reserved.</rights><rights>Springer-Verlag London Ltd., part of Springer Nature 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-c1bbc1999e3297cb6a849a7dc779f574908ac7aecfaedd23b2fa3335cd1db8683</citedby><cites>FETCH-LOGICAL-c372t-c1bbc1999e3297cb6a849a7dc779f574908ac7aecfaedd23b2fa3335cd1db8683</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00170-017-1366-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00170-017-1366-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Mei, Yonggang</creatorcontrib><creatorcontrib>Mo, Rong</creatorcontrib><creatorcontrib>Sun, Huibin</creatorcontrib><creatorcontrib>Bu, Kun</creatorcontrib><title>Chatter detection in milling based on singular spectrum analysis</title><title>International journal of advanced manufacturing technology</title><addtitle>Int J Adv Manuf Technol</addtitle><description>Chatter is a frequently encountered problem in metal cutting field which reduces the machining efficiency and surface quality. Therefore, a reliable and robust chatter detection method is necessary to improve the machining performances. In this work, a novel milling chatter detection approach based on singular spectrum analysis (SSA) is proposed. SSA is applied to process the cutting force signal and extract the feature that is closely related to the machining state. The singular value spectrum obtained by SSA is used to describe the energy distribution of the principal modes in the signal. On the basis of frequency domain chatter theory, singular value entropy (SVE) is adopted to evaluate the variation of energy distribution in the signal and the milling chatter is detected accordingly. Milling experiments under different cutting conditions are performed out to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can accurately identify the onset of chatter. This method is simple in operation and fast in calculation, which makes it have great potential for online chatter detection.</description><subject>CAE) and Design</subject><subject>Chatter</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Cutting force</subject><subject>Cutting parameters</subject><subject>Energy distribution</subject><subject>Engineering</subject><subject>Feature extraction</subject><subject>Industrial and Production Engineering</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Metal cutting</subject><subject>Milling (machining)</subject><subject>Original Article</subject><subject>Signal processing</subject><subject>Spectrum analysis</subject><subject>Surface properties</subject><subject>Vibration</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE1LxDAQhoMouK7-AG8Bz9F8dPNxUxZdhQUveg5pkq5duu2aaQ_996ZU8OReZpjheYfhQeiW0XtGqXoASpmiJBfChJRkPEMLVghBBGWrc7SgXGoilNSX6Apgn2nJpF6gx_WX6_uYcIh99H3dtbhu8aFumrrd4dJBDDjvIE9D4xKGY6bScMCudc0INVyji8o1EG9--xJ9vjx_rF_J9n3ztn7aEi8U74lnZemZMSYKbpQvpdOFcSp4pUy1UoWh2nnloq9cDIGLkldOCLHygYVSSy2W6G6-e0zd9xCht_tuSPkJsHxKK60MP0lxyVmhtVInqWwwC2ViothM-dQBpFjZY6oPLo2WUTtJt7N0m4udpNsxZ_icgcy2u5j-Lv8f-gELxIPJ</recordid><startdate>20180401</startdate><enddate>20180401</enddate><creator>Mei, Yonggang</creator><creator>Mo, Rong</creator><creator>Sun, Huibin</creator><creator>Bu, Kun</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20180401</creationdate><title>Chatter detection in milling based on singular spectrum analysis</title><author>Mei, Yonggang ; Mo, Rong ; Sun, Huibin ; Bu, Kun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-c1bbc1999e3297cb6a849a7dc779f574908ac7aecfaedd23b2fa3335cd1db8683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>CAE) and Design</topic><topic>Chatter</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Cutting force</topic><topic>Cutting parameters</topic><topic>Energy distribution</topic><topic>Engineering</topic><topic>Feature extraction</topic><topic>Industrial and Production Engineering</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Metal cutting</topic><topic>Milling (machining)</topic><topic>Original Article</topic><topic>Signal processing</topic><topic>Spectrum analysis</topic><topic>Surface properties</topic><topic>Vibration</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mei, Yonggang</creatorcontrib><creatorcontrib>Mo, Rong</creatorcontrib><creatorcontrib>Sun, Huibin</creatorcontrib><creatorcontrib>Bu, Kun</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mei, Yonggang</au><au>Mo, Rong</au><au>Sun, Huibin</au><au>Bu, Kun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Chatter detection in milling based on singular spectrum analysis</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2018-04-01</date><risdate>2018</risdate><volume>95</volume><issue>9-12</issue><spage>3475</spage><epage>3486</epage><pages>3475-3486</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>Chatter is a frequently encountered problem in metal cutting field which reduces the machining efficiency and surface quality. Therefore, a reliable and robust chatter detection method is necessary to improve the machining performances. In this work, a novel milling chatter detection approach based on singular spectrum analysis (SSA) is proposed. SSA is applied to process the cutting force signal and extract the feature that is closely related to the machining state. The singular value spectrum obtained by SSA is used to describe the energy distribution of the principal modes in the signal. On the basis of frequency domain chatter theory, singular value entropy (SVE) is adopted to evaluate the variation of energy distribution in the signal and the milling chatter is detected accordingly. Milling experiments under different cutting conditions are performed out to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can accurately identify the onset of chatter. This method is simple in operation and fast in calculation, which makes it have great potential for online chatter detection.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-017-1366-y</doi><tpages>12</tpages></addata></record> |
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| subjects | CAE) and Design Chatter Computer-Aided Engineering (CAD Cutting force Cutting parameters Energy distribution Engineering Feature extraction Industrial and Production Engineering Mechanical Engineering Media Management Metal cutting Milling (machining) Original Article Signal processing Spectrum analysis Surface properties Vibration |
| title | Chatter detection in milling based on singular spectrum analysis |
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