Faculty Publications
Neural Networks-Based In-Process Surface Roughness Adaptive Control System In Turning Operations
Document Type
Conference
Journal/Book/Conference Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
3973 LNCS
First Page
970
Last Page
975
Abstract
Using a back-propagation neural networks algorithm and accelerometer 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. © Springer-Verlag Berlin Heidelberg 2006.
Department
Department of Industrial Technology
Original Publication Date
1-1-2006
DOI of published version
10.1007/11760191_142
Recommended Citation
Zhang, Julie Z. and Chen, Joseph C., "Neural Networks-Based In-Process Surface Roughness Adaptive Control System In Turning Operations" (2006). Faculty Publications. 2862.
https://scholarworks.uni.edu/facpub/2862
