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New classification method of nonstandard parts for CNC selection based on machine learning and geometric analysis.

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Title: New classification method of nonstandard parts for CNC selection based on machine learning and geometric analysis.
Authors: Shi, Peng1, shipeng218@buaa.edu.cn, Tong, Xiaomeng1, tongxiaomeng@buaa.edu.cn, Xu, Yunhong1, susanbuaa1989@gmail.com, Cai, Maolin1, caimaolin@buaa.com.cn
Source: Journal of Intelligent Manufacturing; Jan2026, Vol. 37 Issue 1, p257-280, 24p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 191287665
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: New classification method of nonstandard parts for CNC selection based on machine learning and geometric analysis.
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  Data: <searchLink fieldCode="AU" term="%22Shi%2C+Peng%22">Shi, Peng</searchLink><relatesTo>1</relatesTo>, <i>shipeng218@buaa.edu.cn</i><br /><searchLink fieldCode="AU" term="%22Tong%2C+Xiaomeng%22">Tong, Xiaomeng</searchLink><relatesTo>1</relatesTo>, <i>tongxiaomeng@buaa.edu.cn</i><br /><searchLink fieldCode="AU" term="%22Xu%2C+Yunhong%22">Xu, Yunhong</searchLink><relatesTo>1</relatesTo>, <i>susanbuaa1989@gmail.com</i><br /><searchLink fieldCode="AU" term="%22Cai%2C+Maolin%22">Cai, Maolin</searchLink><relatesTo>1</relatesTo>, <i>caimaolin@buaa.com.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>; Jan2026, Vol. 37 Issue 1, p257-280, 24p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=191287665
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10845-024-02547-4
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      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 257
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      – TitleFull: New classification method of nonstandard parts for CNC selection based on machine learning and geometric analysis.
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            NameFull: Shi, Peng
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            NameFull: Tong, Xiaomeng
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            NameFull: Xu, Yunhong
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              Text: Jan2026
              Type: published
              Y: 2026
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              Value: 37
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