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Prof. Sameh Shawky Habib Abadeer :: Publications:
Baysian neural network model for prediction of WEDM parameters for MMCs.
|Authors:||A. M. Gafeer, S. S. Habib, M. S. Abdel Aziz and T. S. Mahmoud|
|Journal:||Sci. Bull. Fac. Eng. Ain Shams Univ. Part III: Mechanical Engineering and Physics & Mathematics|
|Paper Link:||Not Available|
|Full paper||Sameh Shawky Habib Abadeer_BAYESIAN NEURAL NETWORK MODEL FOR PREDICTION OF WEDM PARAMETERS FOR MMCs1.pdf|
|Supplementary materials||Not Available|
In the present paper, prediction of the wire electrical discharge machining (WEDM) parameters for metal matrix composites (MMCs) was carried out. The prediction model was developed using Bayesian artificial neural networks (ANN) approach. Correlations for the WEDM cutting parameters were obtained for the composites as a function of both the WEDM setting parameters and material variables. The matrix chosen for this work was Al-based AA6063 alloy. In addition to the AA6063 unreinforced alloy, two Al-MMCs containing 10 and 20 vol.-% MgO ceramic particles were investigated. It was found that, the developed model successfully reproduces the machining parameters obtained from the experimental work carried out in this investigation.