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Indirect model for roughness in rough honing processes based on artificial neural networks

Author
Sivatte, M.; Parra, X.; Buj, I.; Joan Vivancos-Calvet
Type of activity
Journal article
Journal
Precision engineering - Journal of the American Society for Precision Engineering (ASPE)
Date of publication
2016-01-01
Volume
43
First page
505
Last page
513
DOI
https://doi.org/10.1016/j.precisioneng.2015.09.004 Open in new window
Project funding
Optimización del acabado superficial interior de cilindros mecanizados mediante honing y plateau-honing
Repository
http://hdl.handle.net/2117/82884 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0141635915001658 Open in new window
Abstract
In the present paper an indirect model based on neural networks is presented for modelling the rough honing process. It allows obtaining values to be set for different process variables (linear speed, tangential speed, pressure of abrasive stones, grain size of abrasive and density of abrasive) as a function of required average roughness Ra. A multilayer perceptron (feedforward) with a backpropagation (BP) training system was used for defining neural networks. Several configurations were tested ...
Citation
Sivatte, M., Llanas, F., Buj, I., Vivancos, J. Indirect model for roughness in rough honing processes based on artificial neural networks. "Precisionn engineering - Journal of the American Society for Precision Engineering (ASPE)", 01 Gener 2016, vol. 43, p. 505-513.
Keywords
Artificial neural networks, Honing, Indirect model, Surface roughness
Group of research
CETpD - Technical Research Centre for Dependency Care and Autonomous Living
ISSET - Integrated Smart Sensors and Health Technologies
TECNOFAB - Manufacturing Technologies Research Group

Participants

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