OPTIMIZATION OF PROCESS VARIABLE FOR ADDITIVE MANUFACTURED PLA BASED TENSILE SPECIMEN USING TAGUCHI DESIGN OF EXPERIMENT AND ARTIFICIAL NEURAL NETWORK (ANN) TECHNIQUE

Abstract

Additive Manufacturing or 3-D printing is a fabrication process by which a 3-dimensional solid iobject iis imanufactured iby idepositing ilayers iby ilayers ion ithe iobject. iIt iis iemerging ias ione iof ithe iinnovative itechnologies iand ihighly iaffect ithe imanufacturing isystem. iToday, iadditive imanufacturing i(AM) ireveals ichanges iin icomplete ivalue icreation, istrategic isystem, iand iprocesses. iMass icustomization iwith iminimization iof iwaste iand iability ito imanufacture icomplex istructure imake ithis iprocess isuperior iover iother. iFreedoms iof idesign, irapid iprototyping iare isome iother ibenefits. i The iaim iof ithis istudy iis ito ioptimize ithe iprocess iparameters iof ifused ideposition imodeling i(FDM) iby iexploring ithe itensile itesting iof iPolylactic iacid i(PLA). iIn ithis iwork, iwe ivaried iprocess iparameters ilike ilayer ithickness, iraster iorientation, istructure, ispeed, iand inozzle itemperature iare iinvestigated. iBased ion ithese iparameters iTensile ispecimen iare iprinted iby iusing iFusebot i250+ iFDM. iThe iTensile ibehavior iwas iinvestigated iunder idifferent icondition. iUsing iArtificial iNeural iNetwork, ithe iobtained iexperimental iresults iare ivalidated iand ichecked ifor ioptimum ivalue iof itensile istrength iunder idefined iconditions.