NDLI: Retrieval of machining information from feature patterns using artificial neural networks

Content Provider	Springer Nature Link
Author	Chakraborty, Shankar Basu, Arit
Copyright Year	2005
Abstract	The integration of design and manufacturing has been the subject of much debate and discussion over a long period of time. Recognition of feature patterns and the retrieval of necessary machining information from those patterns play vital roles in this process of integration, as they facilitate the selection of the necessary manufacturing parameters required to transform the designed product into a final physical entity. Although the problem of recognising features from a solid model has been exclusively studied, most existing product models are expressed as engineering drawings. Moreover, the solid model can only provide complete 3D topological and geometrical data and some of the essential machining information cannot be retrieved. In this paper, an approach for defining engineering features, like slots, steps and circular pockets is proposed using binary strings. Two artificial neural networks, one for slots and steps and the other for circular pockets, are designed and developed. These neural networks take the binary strings as inputs and give the relevant machining information as outputs. The networks are trained with non-interacting features and after training, those will become capable of providing the necessary machining information for both non-interacting and interacting features in the domains of slots, steps and circular pockets. This novel approach can further be extended to other features for retrieving relevant machining information and thus facilitating the effective integration of design and manufacturing.
Starting Page	781
Ending Page	787
Page Count	7
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	27
Issue Number	7-8
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2005-02-23
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural networks Feature recognition Interacting features Non-interacting features Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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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
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