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Prediction of the Optimum Cutting Parameters of EDM Process Applied for Al-Sic Reinforced Metal Matrix Composite Using Neural Network

Hany A. Shehata, Samy J. Ebeid, A. M. Kohail

Abstract


This paper describes the prediction of the input parameters of EDM Process for composite materials using intelligent technique. Stir casting method (SCM) was used to produce metal matrix composites (MMC). Aluminum (Al) 6061 and silicon carbide particles (F500≈15µm) were selected as matrix and reinforcement materials respectively. Matrix, Al-5%SiC and Al-10%SiC were subjected to Electric discharge machining (EDM) to analyses the effect of input parameters namely peak current (Ip), pulse-on-time (Ton), duty cycle (DT) and gap voltage (Vg). Optical microscope was used to determine the SiC particles distribution in the Al matrix of the composites (as-cast). A digital balance was used to determine the material removal rate (MRR) and Tool wear (TWR) for the matrix and composites. Surface roughness measurement tester used to determine the surface roughness (Ra) for the matrix and composites. An Artificial Neural Network (ANN) was applied on the EDM process to predict the input parameters.  


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References


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