|About this Abstract
||2018 TMS Annual Meeting & Exhibition
||Computational Design and Simulation of Materials (CDSM 2018): Computational Design of Materials
||Prediction of the Abrasive Wear Behaviour of Heat Treated Aluminium-clay Composites
||Ademola Abiona Agbeleye, David E. Esezobor, Johnson Olumuyiwa Agunsoye, Olawale Sanmbo Balogun, Adeyanju Azeez Sosimi
|On-Site Speaker (Planned)
||Ademola Abiona Agbeleye
Artificial neural networks due to their capabilities of handling nonlinear behaviour, learning from experimental data and generalization, have offered great prospect in developing mathematical wear models. In the present work the potential of using back propagation neural network with 4-10-1 architecture has been explored to predict the wear rate of the heat treated aluminium-clay composites under dry sliding conditions. The results show that the performance of Levenberg-Marquardt (LM) training algorithm is superior to all other algorithms. The predicted data are perfectly acceptable when compared to the actual experimental test results. Finally, a well-trained artificial neural network system developed for estimating the wear rate in the complex three-body abrasive wear situation of aluminium metal matrix composite can be handy for an optimum design as well as being an alternative and practical technique to evaluate the wear rate.
Keywords: automobile, friction, quenching, model, morphology.
||Planned: Supplemental Proceedings volume