An Intelligent Process Planning Method Based on Feature-based History Machining Data for Aircraft Structural Parts-Reference-Cited by-同舟云学术

An Intelligent Process Planning Method Based on Feature-based History Machining Data for Aircraft Structural Parts

Published:2016 Issue: Volume:56 Page:585-589
ISSN:2212-8271
Container-title:Procedia CIRP
language:en
Short-container-title:Procedia CIRP

Author:

Gao Xin,Mou Wenping,Peng Yu

Publisher

Elsevier BV

Subject

General Medicine

Reference16 articles.

1. Konwledge framework for intelligent manufacturing systems;Ricardo;Journal of Intelligent Manufacturing,2011

2. Li Y, Liu Q, Xiong J, et al. Research on data-sharing and intelligent CNC machining system. In 2015 IEEE international conference on mechatronics and automation (ICMA). doi: 10.1109/ICMA.2015.7237557.

3. Use of NC kernel data for surface roughness monitoring in milling operations;Christian;The International Journal of Advanced Manufacturing Technology,2011

4. A manufacturing resource allocation method with konwledge-based fuzzy comprehensive evaluation for aircraft structural parts;Chu;International Journal of Production Research,2014

5. Parametric process planning based on feature parameters of parts;Chen;The International Journal of Advanced Manufacturing Technology,2006

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