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A B S T R A C T  A R T I C L E I N F O Aluminiumalloywithsiliconcarbideparticulate(Al/SiCp)reinforcedmetal matrixcomposite(MMC)areusedwithinavarietyofengineeringapplications duetotheirexcellentpropertiesincomparisonwithnon-reinforcedalloys. Thispresentedworkattemptedthedevelopmentofpredictivemodelingand optimizationofprocessparametersintheturningofAl/SiCpMMCusinga titaniumnitride(TiN)coatedcarbidetool.ThesurfaceroughnessRaasprod- uctqualityandtoolwearVBforimprovedtoollifewereconsideredastwo processresponsesandtheprocessparameterswerecuttingspeedv,feedf, anddepthofcutd.Twomodelingtechniquesviz.,responsesurfacemethodol- ogy(RSM)andartificialneuralnetwork(ANN)wereemployedfordeveloping RaandVBpredictivemodelsandtheirpredictivecapabilitiescompared.Four differentRSMmodelsweretriedoutviz.,linear,linearwithinteraction,linear withsquare,andquadraticmodels.Thelinearwithinteractionmodelwas

advances in production engineering and management

Modeling and optimization of parameters for minimizing surface roughness and tool wear in turning Al/SiCp MMC, using conventional and soft computing techniques