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1、neuralnetworks,machinelearning,heuristicsearch,CAD/CAM*ZsoltJ.VIHAROSLászlóMONOSTORI*SándorMARKOS**SELECTIONOFINPUTANDOUTPUTVARIABLESFORANNBASEDMODELINGOFCUTTINGPROCESSESModelingofmanufacturingoperationsisanimportanttoolforproductionplanning,optimizatio
2、nandcontrol.Artificialneuralnetworks(ANNs)canhandlestrongnon-linearity,largenumberofparameters,missinginformation.BasedontheirinherentlearningcapabilitiesANNscanadaptthemselvestochangesoftheproductionenvironmentandcanbeusedalsointhecaseifthereisnoexactk
3、nowledgeabouttherelationshipsbetweenvariousparametersofmanufacturing.TypicalfieldofANNbasedoperationmodelingiscutting.Therelationshipsofthephysicalphenomenaincorporatedintothecuttingoperationareverycomplex.Intheapplicationofthesemodelsseveraltaskscanbed
4、etermined:e.g.?intheplanningphasethesurfaceroughnessispredefinedandthemodelisexpectedtoselectthecuttingparametersandtopredictthecuttingforce,while?duringsupervisedproductionthecuttingparametersareknownande.g.thecuttingforceismeasuredandtheproducedsurfac
5、eroughnessistobeestimated.Intheaboveassignments,theoperationparametersarethesamebuttheoperationmodelhasothervariablesontheinputandontheoutputsides.Inthispaperamethodispresentedtobuildageneraloperationmodelwiththerequestedaccuracy.Thismethodincorporates:
6、?determinationofthenumberofoutputvariables?determinationforeveryparametertobeinputoroutputThemethodisalsousefulinthecaseofstrongnonlinearrelationships.Experimentsshowtheapplicabilityoftheapproach.1.MODELINGOFPRODUCTIONCHAINS–THEMAINASSIGNMENTModellingme
7、thodscanbeusedinseveralfieldsofproductione.g.inplanning,optimisationorcontrol.Theproductioninourdaysincorporatesseveralstages,theworkpiecegoesthroughanumberofoperations(Fig.1.).Theoutputofoneoperationistheinputofananotheroneoritisafeatureoftheendproduct
8、.Tobuildamodelforaproductionchain,modelshavetobeorderedtoeverystageofproduction.Achainofoperationsconnectedbytheirinput-outputparameterscanmodel*ComputerandAutomationResearchInstitute,HungarianAcademyofSciences,Kendeu.13-17,H-111