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Adaptive indirect neural network model for roughness in honing processes

Author
Buj-Corral, I.; Sivatte, M.; Parra, X.
Type of activity
Journal article
Journal
Tribology international
Date of publication
2020-01-01
Volume
141
Number
January
First page
05891:1
Last page
05891:10
DOI
10.1016/j.triboint.2019.105891
Repository
http://hdl.handle.net/2117/176497 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0301679X19304104 Open in new window
Abstract
Honing processes provide a crosshatch pattern that allows oil flow, for example in combustion engine cylinders. This paper provides an adaptive neural network model for predicting roughness as a function of process parameters. Input variables are three parameters from the Abbott-Firestone curve, Rk, Rpk and Rvk. Output parameters are grain size, density of abrasive, pressure, linear speed and tangential speed. The model consists of applying a direct and an indirect model consecutively, with one ...
Citation
Buj-Corral, I.; Sivatte, M.; Parra, X. Adaptive indirect neural network model for roughness in honing processes. "Tribology international", 1 Gener 2020, vol. 141, núm. January, p. 05891:1-05891:10.
Keywords
Adaptive control, Artificial neural networks, Honing, Surface roughness
Group of research
CETpD - Technical Research Centre for Dependency Care and Autonomous Living
TECNOFAB - Manufacturing Technologies Research Group

Participants