Dynamic design method of digital twin process model driven by knowledge-evolution machining features
Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It...
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Veröffentlicht in: | International journal of production research 2022-04, Vol.60 (7), p.2312-2330 |
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creator | Liu, Jinfeng Zhao, Peng Jing, Xuwen Cao, Xuwu Sheng, Sushan Zhou, Honggen Liu, Xiaojun Feng, Feng |
description | Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It inevitably causes time and economic losses for the enterprise. In order to express the evolutionary characteristics of product processing, the construction method of digital twin process model (DTPM) is proposed based on the knowledge-evolution machining features. Three key technologies include correlation structure of process knowledge, expression method of the evolution geometric features and the association mechanism between two are solved. On this basis, the construction framework of DTPM is illustrated. Then, the organisation and management mechanism of multi-source heterogeneous data is discussed in detail. At last, a case study of the complex machined part is researched, the results show that the processing time reduced by about 7% and the processing stability improved by 40%. Meanwhile, the implementation scheme, application process and effect of this case are described in detail to provide reference for enterprises. |
doi_str_mv | 10.1080/00207543.2021.1887531 |
format | Article |
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Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It inevitably causes time and economic losses for the enterprise. In order to express the evolutionary characteristics of product processing, the construction method of digital twin process model (DTPM) is proposed based on the knowledge-evolution machining features. Three key technologies include correlation structure of process knowledge, expression method of the evolution geometric features and the association mechanism between two are solved. On this basis, the construction framework of DTPM is illustrated. Then, the organisation and management mechanism of multi-source heterogeneous data is discussed in detail. At last, a case study of the complex machined part is researched, the results show that the processing time reduced by about 7% and the processing stability improved by 40%. Meanwhile, the implementation scheme, application process and effect of this case are described in detail to provide reference for enterprises.</description><identifier>ISSN: 0020-7543</identifier><identifier>EISSN: 1366-588X</identifier><identifier>DOI: 10.1080/00207543.2021.1887531</identifier><language>eng</language><publisher>London: Taylor & Francis</publisher><subject>Digital twin ; Digital twins ; dynamic evolution ; Economic impact ; Evolution ; Machining ; machining features ; process knowledge</subject><ispartof>International journal of production research, 2022-04, Vol.60 (7), p.2312-2330</ispartof><rights>2021 Informa UK Limited, trading as Taylor & Francis Group 2021</rights><rights>2021 Informa UK Limited, trading as Taylor & Francis Group</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-9c141e64ee3f5df27e2f4c465f530cf7d0b3953b15a89a27061685ce01d5f8b53</citedby><cites>FETCH-LOGICAL-c363t-9c141e64ee3f5df27e2f4c465f530cf7d0b3953b15a89a27061685ce01d5f8b53</cites><orcidid>0000-0003-1900-571X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1887531$$EPDF$$P50$$Ginformaworld$$H</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/00207543.2021.1887531$$EHTML$$P50$$Ginformaworld$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,59647,60436</link.rule.ids></links><search><creatorcontrib>Liu, Jinfeng</creatorcontrib><creatorcontrib>Zhao, Peng</creatorcontrib><creatorcontrib>Jing, Xuwen</creatorcontrib><creatorcontrib>Cao, Xuwu</creatorcontrib><creatorcontrib>Sheng, Sushan</creatorcontrib><creatorcontrib>Zhou, Honggen</creatorcontrib><creatorcontrib>Liu, Xiaojun</creatorcontrib><creatorcontrib>Feng, Feng</creatorcontrib><title>Dynamic design method of digital twin process model driven by knowledge-evolution machining features</title><title>International journal of production research</title><description>Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It inevitably causes time and economic losses for the enterprise. In order to express the evolutionary characteristics of product processing, the construction method of digital twin process model (DTPM) is proposed based on the knowledge-evolution machining features. Three key technologies include correlation structure of process knowledge, expression method of the evolution geometric features and the association mechanism between two are solved. On this basis, the construction framework of DTPM is illustrated. Then, the organisation and management mechanism of multi-source heterogeneous data is discussed in detail. At last, a case study of the complex machined part is researched, the results show that the processing time reduced by about 7% and the processing stability improved by 40%. Meanwhile, the implementation scheme, application process and effect of this case are described in detail to provide reference for enterprises.</description><subject>Digital twin</subject><subject>Digital twins</subject><subject>dynamic evolution</subject><subject>Economic impact</subject><subject>Evolution</subject><subject>Machining</subject><subject>machining features</subject><subject>process knowledge</subject><issn>0020-7543</issn><issn>1366-588X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKs_QQh43ppskt3sTamfIHhR8BbSZNJGd5Oa7Fr6791SxZtzmcvzvDO8CJ1TMqNEkktCSlILzmYlKemMSlkLRg_QhLKqKoSUb4dosmOKHXSMTnJ-J-MIySfI3myD7rzBFrJfBtxBv4oWR4etX_pet7jf-IDXKRrIGXfRQott8l8Q8GKLP0LctGCXUMBXbIfexzFCm5UPPiyxA90PCfIpOnK6zXD2s6fo9e72Zf5QPD3fP86vnwrDKtYXjaGcQsUBmBPWlTWUjhteCScYMa62ZMEawRZUaNnosiYVraQwQKgVTi4Em6KLfe747ucAuVfvcUhhPKnKuiaSVpzLkRJ7yqSYcwKn1sl3Om0VJWpXqPotVO0KVT-Fjh7ee2Bi8PnPkrSWjWx4MyJXe8QHF1OnNzG1VvV628bkkg5m1Nj_V74B-LeIHA</recordid><startdate>20220403</startdate><enddate>20220403</enddate><creator>Liu, Jinfeng</creator><creator>Zhao, Peng</creator><creator>Jing, Xuwen</creator><creator>Cao, Xuwu</creator><creator>Sheng, Sushan</creator><creator>Zhou, Honggen</creator><creator>Liu, Xiaojun</creator><creator>Feng, Feng</creator><general>Taylor & Francis</general><general>Taylor & Francis LLC</general><scope>OQ6</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0003-1900-571X</orcidid></search><sort><creationdate>20220403</creationdate><title>Dynamic design method of digital twin process model driven by knowledge-evolution machining features</title><author>Liu, Jinfeng ; Zhao, Peng ; Jing, Xuwen ; Cao, Xuwu ; Sheng, Sushan ; Zhou, Honggen ; Liu, Xiaojun ; Feng, Feng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-9c141e64ee3f5df27e2f4c465f530cf7d0b3953b15a89a27061685ce01d5f8b53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Digital twin</topic><topic>Digital twins</topic><topic>dynamic evolution</topic><topic>Economic impact</topic><topic>Evolution</topic><topic>Machining</topic><topic>machining features</topic><topic>process knowledge</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Jinfeng</creatorcontrib><creatorcontrib>Zhao, Peng</creatorcontrib><creatorcontrib>Jing, Xuwen</creatorcontrib><creatorcontrib>Cao, Xuwu</creatorcontrib><creatorcontrib>Sheng, Sushan</creatorcontrib><creatorcontrib>Zhou, Honggen</creatorcontrib><creatorcontrib>Liu, Xiaojun</creatorcontrib><creatorcontrib>Feng, Feng</creatorcontrib><collection>ECONIS</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering 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>International journal of production research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Jinfeng</au><au>Zhao, Peng</au><au>Jing, Xuwen</au><au>Cao, Xuwu</au><au>Sheng, Sushan</au><au>Zhou, Honggen</au><au>Liu, Xiaojun</au><au>Feng, Feng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic design method of digital twin process model driven by knowledge-evolution machining features</atitle><jtitle>International journal of production research</jtitle><date>2022-04-03</date><risdate>2022</risdate><volume>60</volume><issue>7</issue><spage>2312</spage><epage>2330</epage><pages>2312-2330</pages><issn>0020-7543</issn><eissn>1366-588X</eissn><abstract>Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It inevitably causes time and economic losses for the enterprise. In order to express the evolutionary characteristics of product processing, the construction method of digital twin process model (DTPM) is proposed based on the knowledge-evolution machining features. Three key technologies include correlation structure of process knowledge, expression method of the evolution geometric features and the association mechanism between two are solved. On this basis, the construction framework of DTPM is illustrated. Then, the organisation and management mechanism of multi-source heterogeneous data is discussed in detail. At last, a case study of the complex machined part is researched, the results show that the processing time reduced by about 7% and the processing stability improved by 40%. 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subjects | Digital twin Digital twins dynamic evolution Economic impact Evolution Machining machining features process knowledge |
title | Dynamic design method of digital twin process model driven by knowledge-evolution machining features |
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