EconPapers    
Economics at your fingertips  
 

A novel 2.5D machining feature recognition method based on ray blanking algorithm

Peng Shi (), Xiaomeng Tong (), Maolin Cai () and Shuai Niu ()
Additional contact information
Peng Shi: Beihang University
Xiaomeng Tong: Beihang University
Maolin Cai: Beihang University
Shuai Niu: Beihang University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 9, 1585-1605

Abstract: Abstract Feature recognition (FR) is one of the main tasks involved in computer-aided design, computer aided process planning, and computer-aided manufacturing systems. Conventional FR methods have topology, voxel, and pixel as model input data, which are rule-based, body decomposition-based, and neural network-based, respectively. However, FR methods are mostly applied to identify geometric features and are rarely manufacturing oriented. Recognizable feature types depend on the establishment of a feature database, which can easily lead to complex FR errors or omissions. This study proposes a novel recognition method for the general machining feature of 2.5-axis, one of the basic and commonly encountered feature types in manufacture industries. A novel ray fading algorithm is proposed to calculate the feature machining direction, and the type of 2.5-axis machining features is determined by both machining direction and topology. Features with machining directions can effectively assist the intelligent process planning to reduce the clamping changes and can potentially lead to significant time reduction for part machining.

Keywords: 2.5 axis machining feature; Blanking algorithm; Feature recognition; Computer-aided manufacturing (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02122-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02122-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02122-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-04-12
Handle: RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02122-3