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Interacting machining feature recognition considering machining process based on heuristic hint.

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Title: Interacting machining feature recognition considering machining process based on heuristic hint.
Authors: Zhu, Zhi-Yong1 (AUTHOR) zhuzhiyzzy@foxmail.com, Wang, Qing-Hui1 (AUTHOR) wqh@scut.edu.cn, Li, Jing-Rong2 (AUTHOR) lijr@scut.edu.cn, Li, Yue-Feng1 (AUTHOR) lucaslyf@foxmail.com, Xie, Hai-Long2 (AUTHOR) hlxie@scut.edu.cn
Source: International Journal of Advanced Manufacturing Technology. May2026, Vol. 144 Issue 5/6, p3511-3529. 19p.
Subjects: Machining, Heuristic, Computer-aided process planning, Mass customization
Abstract: Machining feature recognition (MFR) technology is crucial for intelligent quotation, intelligent process planning and toolpath generation at mass personalized customization paradigm. However, most existing MFR methods rely on predefined information and are restricted to specific feature types, and the recognized features often require further decomposition. Additionally, existing recognition processes do not consider machining processing information, so that most recognized machining features cannot be directly used for subsequent toolpath generation. In this work, a new MFR method is proposed based on so-called heuristic hint search strategy, which comes with the advantage of being unrestricted by feature types and recognized features are independent features. Furthermore, a novel interacting machining feature processing approach is proposed based on a z-direction layered method, which analyzes the relationship between the contour of the machining features and the cutting area of the part to optimize the recognized features. This makes the MFR intrinsically associated with subsequent toolpath generation for machining. Thousands of industrial models have been used to validate that the proposed method can improve the MFR accuracy to assist the machining process and potentially reduce the machining time of parts. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 194518417
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Interacting machining feature recognition considering machining process based on heuristic hint.
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  Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Zhi-Yong%22">Zhu, Zhi-Yong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhuzhiyzzy@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Qing-Hui%22">Wang, Qing-Hui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wqh@scut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jing-Rong%22">Li, Jing-Rong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> lijr@scut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Yue-Feng%22">Li, Yue-Feng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lucaslyf@foxmail.com</i><br /><searchLink fieldCode="AR" term="%22Xie%2C+Hai-Long%22">Xie, Hai-Long</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hlxie@scut.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. May2026, Vol. 144 Issue 5/6, p3511-3529. 19p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Machining%22">Machining</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+process+planning%22">Computer-aided process planning</searchLink><br /><searchLink fieldCode="DE" term="%22Mass+customization%22">Mass customization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Machining feature recognition (MFR) technology is crucial for intelligent quotation, intelligent process planning and toolpath generation at mass personalized customization paradigm. However, most existing MFR methods rely on predefined information and are restricted to specific feature types, and the recognized features often require further decomposition. Additionally, existing recognition processes do not consider machining processing information, so that most recognized machining features cannot be directly used for subsequent toolpath generation. In this work, a new MFR method is proposed based on so-called heuristic hint search strategy, which comes with the advantage of being unrestricted by feature types and recognized features are independent features. Furthermore, a novel interacting machining feature processing approach is proposed based on a z-direction layered method, which analyzes the relationship between the contour of the machining features and the cutting area of the part to optimize the recognized features. This makes the MFR intrinsically associated with subsequent toolpath generation for machining. Thousands of industrial models have been used to validate that the proposed method can improve the MFR accuracy to assist the machining process and potentially reduce the machining time of parts. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00170-026-18082-w
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 3511
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      – SubjectFull: Machining
        Type: general
      – SubjectFull: Heuristic
        Type: general
      – SubjectFull: Computer-aided process planning
        Type: general
      – SubjectFull: Mass customization
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            NameFull: Zhu, Zhi-Yong
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            NameFull: Wang, Qing-Hui
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            NameFull: Li, Jing-Rong
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            NameFull: Li, Yue-Feng
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            NameFull: Xie, Hai-Long
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            – D: 20
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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