In this study, an approach for calling the three-body nasty wear behavior of unfilled and element filled element fabric supported epoxy composite using two displaying techniques - Taguchi reasoning and artificial interconnected system are presented. A set of experiments were transported using an orthogonal array established Taguchi techniques to achieve data in a reserved manner. The results showed that the adding of graphite coarse into carbon binding material composite led to a decrease in its caustic wear resistance, and the wear deficit increased accompanying an increase in abrading distance and loads. To investigate the effect of control limits on the wear behavior of the composites, an reasoning of variance was acted, and the S/N ratio was determined. The results found that the normal load had the best physical in addition to statistical influence on the nasty wear of the composites followed by abrading distance and stuffing content. To predict the wear possessions of composites as a function of testing environments, 3-[5]1-1 neural network architecture accompanying Levenberg Marquardt (LM) training treasure was used. By equating the correlations obtained by Taguchi reversion analysis and affected neural network accompanying the experimental results it was raise that the artificial neural network thinks the wear rate better than regression reasoning. Therefore, a well-trained pretended neural network system maybe very helpful in judging the weight deficit in the complex three-body abrasive wear position of polymer composites.
Author(s) Details:
K. Sudarshan Rao,
Department of Mechanical Engineering, Shri
Madhwa Vadiraja Institute of Technology and Management, Bantakal, Udupi,
Karnataka, India.
Please see the link here: https://stm.bookpi.org/RPST-V7/article/view/9949
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