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Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach
Faculty
Science
Year:
2008
Type of Publication:
Article
Pages:
271-278
Authors:
Khashaba, U. A, Selmy, A. I, El-Sonbaty, I. A, Ali, A. I
DOI:
10.1016/j.jmatprotec.2007.09.006
Journal:
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY ELSEVIER SCIENCE SA
Volume:
200
Research Area:
Engineering; Materials Science
ISSN
ISI:000254814100029
Keywords :
surface roughness profile, neural network, Fractal geometry approach, milling operation, speed, feed depth of cut
Abstract:
Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the ``ANNs{''} models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension ``D{''} and vertical scaling parameter ``G{''}. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters ``D{''} and ``G{''} in face milling operations. Moreover, W-M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens. (c) 2007 Elsevier B.V. All rights reserved.
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