Neural networks-based in-process surface roughness adaptive control system in turning operations

Authors:
Julie Z. Zhang;Joseph C. Chen
Affiliations:
37 ITC, University of Northern Iowa, Cedar Falls, IA;221 I. Ed. II, Iowa State University, Ames, IA
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 2
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Abstract

Using a back-propagation neural networks algorithm and accelero-meter sensor technique, this research developed an in-process surface roughness adaptive control (IPSRAC) system in turning operations. This system not only can predict surface roughness in real time, but can also provide an adaptive feed rate change in finishing turning to ensure the surface roughness can meet requirements.