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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]
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Database: Engineering Source
Description
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]
ISSN:02683768
DOI:10.1007/s00170-026-18082-w