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A B S T R A C T A R T I C L E I N F O Inthepresentwork,surfaceroughnessprediction modelin cylindricalgrind- ingofLM25/SiC/4pm etalmatrixcomposites (MMC) wasdevelopedusing artificialneuralnetwork(ANN)methodology.Theindependentinputmachin- ingparametersconsideredinthemodelingwerewheelvelocity,feed,work piecevelocityanddepthofcut.Theneuralnetworkarchitecture4-12-1with logsigtransferfunctionwasfoundoptimumwith94.20%modelaccuracy. Theanalysisofvariance(ANOVA)wascarriedtostudyinfluenceofthema- chiningparametersonsurfaceroughness.ThestudyrevealedhigherF-ratio forwheelvelocityanditfoundtobethemostinfluencingparameterinpre- dictionofsurfaceroughness.Thepercentageofcontributionforwheelveloci- tywas32.47%,feedwas26.50%andworkpiecevelocitywas25.08%.The depthofcutwasfoundtohaveleasteffectonsurfaceroughnesswith13.22% contribution.Theindependentandcombinedeffectofprocessparameterson predictedvalueofsurfaceroughnesswasstudiedusingtwo-dimensional graphsandsurfaceplots.Thestudyshowedthatsurfaceroughnessincreases asfeedincreaseswhileitdecreaseswithincreaseinwheelvelocity.Itwas alsoobservedthatminimumsurfacefinishcouldbeobtainedathighwheel andworkpiecevelocities,andlowfeedanddepthofcut.

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al-SiCp metal matrix composites and ANOVA analysis