NDLI: Recognizing 2.5D manufacturing feature using neural network

Content Provider	IEEE Xplore Digital Library
Author	Xiaojun Liu Hong Yi Zhonghua Ni Xiaoli Qiu Tingyu Liu
Copyright Year	2008
Description	Author affiliation: Sch. of Mech. Eng., Southeast Univ., Nanjing (Xiaojun Liu; Hong Yi; Zhonghua Ni; Xiaoli Qiu; Tingyu Liu)
Abstract	This study develops a neural network based methodology for recognizing manufacturing feature (MF) using the boundary representation (B-rep) information. The methodology is capable of recognizing not only basic MF (including standard MF and non-standard MF) but also interacting MF. Firstly, both the edges' convex concave attribute and the edge's position (whether an edges belongs to the inner edge loop or outer edge loop), which reflects the edges' characteristics and the relationship between the bottom profile and the adjacent faces, were analyzed to defined the input vector for the neural network. Based on this, a BP neural network with a single hidden layer which contained 10 neurons was obtained. The basic MF is divided into additive MF (AMF), throuth subtractive closed MF (through SCMF), blind subtractive closed MF (blind SCMF), subtractive opened MF (SOMF) four groups, and can be recognized easily using the neural network has been trained. For the interacting MF, a basic MF is recognized firstly, the bottom profile is updated when a MF interactive with the recognized basic MF is identified. The height or depth of the MF is determined by the height of the lowest adjacent face. The MF has been recognized will be added to the part, the following recognition will be carried on based on the new part model.
Starting Page	311
Ending Page	316
File Size	4082683
Page Count	6
File Format	PDF
ISBN	9781424437795
DOI	10.1109/MMVIP.2008.4749552
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-02
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Intl Journal of Intelligent
Subject Keyword	CAD/CAPP/CAM CADCAM Mechatronics Computer aided manufacturing Face recognition Artificial neural networks Machining neural networks Mechanical engineering Machine vision Neural networks manufacturing feature Solids feature recognition
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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