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TextileResearchJournalArticle

UseofArtificialNeuralNetworksforDeterminingtheLeveling

ActionPointattheAuto-levelingDrawFrame

AssadFarooq1andChokriCherif

InstituteofTextileandClothingTechnology,Technische

UniversitätDresden.Dresden,Germany

Abstract

Artificialneuralnetworkswiththeirabilityoflearningfromdatahavebeensuccessfullyappliedinthetextileindustry.Thelevelingactionpointisoneoftheimportantauto-levelingparametersofthedrawingframeandstronglyinfluencesthequalityofthemanufacturedyarn.Thispaperreportsamethodofpredictingthelevelingactionpointusingartificialneuralnetworks.Variouslevelingactionpointaffectingvariableswereselectedasinputsfortrainingtheartificialneuralnetworkswiththeaimtooptimizetheauto-levelingbylimitingthelevelingactionpointsearchrange.TheLevenbergMarquardtalgorithmisincorporatedintotheback-propagationtoacceleratethetrainingandBayesianregularizationisappliedtoimprovethegeneralizationofthenetworks.Theresultsobtainedarequitepromising.

Keywords:

artificialneuralnetworks,auto-lev-eling,drawframe,levelingactionpoint。

Theevennessoftheyarnplaysanincreasinglysignificantroleinthetextileindustry,whilethesliverevennessisoneofthecriticalfactorswhenproducingqualityyarn.Thesliverevennessisalsothemajorcriteriafortheassessmentoftheoperationofthedrawframe.Inprinciple,therearetwoapproachestoreducethesliverirregularities.Oneistostudythedraftingmechanismandrecognizethecausesforirregularities,sothatmeansmaybefoundtoreducethem.Theothermorevaluableapproachistouseauto-levelers[1],sinceinmostcasesthedoublingisinadequatetocorrectthevariationsinsliver.Thecontrolofsliverirregularitiescanlowerthedependenceoncardsliveruniformity,ambientconditions,andframeparameters.

Attheauto-levelerdrawframe（RSB-D40）thethicknessvariationsinthefedsliverarecontinuallymonitoredbyamechanicaldevice（atongue-grooveroll）andsubsequentlyconvertedintoelectricalsignals.Themeasuredvaluesaretransmittedtoanelectronicmemorywithavariable,thetimedelayedresponse.Thetimedelayallowsthedraftbetweenthemid-rollandthedeliveryrollofthedrawframetoadjustexactlyatthatmomentwhenthedefectivesliverpiece,whichhadbeenmeasuredbyapairofscanningrollers,findsitselfatapointofdraft.Atthispoint,aservomotoroperatesdependingupontheamountofvariationdetectedinthesliverpiece.Thedistancethatseparatesthescanningrollerspairandthepointofdraftiscalledthezeropointofregulationorthelevelingactionpoint（LAP）asshowninFigure1.Thisleadstothecalculatedcorrectiononthecorrespondingdefectivematerial[2,3].Inauto-levelingdrawframes,especiallyinthecaseofachangeoffibermaterial,orbatchesthemachinesettingsandprocesscontrollingparametersmustbeoptimized.TheLAPisthemostimportantauto-levelingparameterwhichisinfluencedbyvariousparameterssuchasfeedingspeed,material,breakdraftgauge,maindraftgauge,feedingtension,breakdraft,andsettingofthesliverguidingrollersetc.

UseofArtificialNeuralNetworksforDeterminingtheLevelingActionPointA.FarooqandC.Cherif

Figure1Schematicdiagramofanauto-levelerdrawingframe.

Previously,thesliversampleshadtobeproducedwithdifferentsettings,takentothelaboratory,andexaminedontheevennesstesteruntiltheoptimumLAPwasfound（manualsearch）.Auto-levelerdrawframeRSB-D40implementsanautomaticsearchfunctionfortheoptimumdeterminationoftheLAP.Duringthisfunction,thesliverisautomaticallyscannedbyadjustingthedifferentLAPstemporarilyandtheresultedvaluesarerecorded.Duringthisprocess,thequalityparametersareconstantlymonitoredandanalgorithmautomaticallycalculatestheoptimumLAPbyselectingthepointwiththeminimumsliverCV%.Atpresentasearchrangeof120mmisscanned,i.e.21pointsareexaminedusing100mofsliverineachcase;therefore2100mofsliverisnecessarytocarryoutthesearchfunction.Thisisaverytime-consumingmethodaccompaniedbythematerialandproductionlosses,andhencedirectlyaffectingthecostparameters.Inthiswork,wehavetriedtofindoutthepossibilityofpredictingtheLAP,usingartificialneuralnet-works,tolimittheautomaticsearchspanandtoreducetheabove-mentioneddisadvantages.

ArtificialNeuralNetworks

Themotivationofusingartificialneuralnetworksliesintheirflexibilityandpowerofinformationprocessingthatconventionalcomputingmethodsdonothave.Theneuralnetworksystemcansolveaproblem“byexperienceandlearning”theinput–outputpatternsprovidedbytheuser.Inthefieldoftextiles,artificialneuralnetworks（mostlyusingback-propagation）havebeenextensivelystudiedduringthelasttwodecades[4–6].Inthefieldofspinningpreviousresearchhasconcentratedonpredictingtheyarnpropertiesandthespinningprocessperformanceusingthefiberpropertiesoracombinationoffiberpropertiesandmachinesettingsastheinputofneuralnetworks[7–12].Back-propagationisasupervisedlearningtechniquemostfrequentlyusedforartificialneuralnetworktraining.Theback-propagationalgorithmisbasedontheWidrow-Hoffdeltalearningruleinwhichtheweightadjustmentiscarriedoutthroughthemeansquareerroroftheoutputresponsetothesampleinput[13].Thesetofthesesamplepatternsisrepeatedlypresentedtothenetworkuntiltheerrorvalueisminimized.Theback-propagationalgorithmusesthesteepestdescentmethod,whichisessentiallyafirst-ordermethodtodetermineasuitabledirectionofgradientmovement.

Overfitting

Thegoalofneuralnetworktrainingistoproduceanetworkwhichproducessmallerrorsonthetrainingset,andwhichalsorespondsproperlytonovelinputs.Whenanetworkperformsaswellonnovelinputsasontrainingsetinputs,thenetworkissaidtobewellgeneralized.Thegeneralizationcapacityofthenetworkislargelygovernedbythenetworkarchitecture（numberofhiddenneurons）andthisplaysavitalroleduringthetraining.Anetworkwhichisnotcomplexenoughtolearnalltheinformationinthedataissaidtobeunderfitted,whileanetworkthatistoocomplextofitthe“noise”inthedataleadstooverfitting.“Noise”meansvariationinthetargetvaluesthatareunpredictablefromtheinputsofaspecificnetwork.Allstandardneuralnetworkarchitecturessuchasthefullyconnectedmulti-layerperceptronarepronetooverfitting.Moreover,itisverydifficulttoacquirethenoisefreedatafromthespinningindustryduetothedependenceofendproductsontheinherentmaterialvariationsandenvironmentalconditions,etc.Earlystoppingisthemostcommonlyusedtechniquetotacklethisproblem.Thisinvolvesthedivisionoftrainingdataintothreesets,i.e.atrainingset,avalidationsetandatestset,withthedrawbackthatalargepartofthedata（validationset）canneverbethepartofthetraining.

Regularization

Theothersolutionofoverfittingisregularization,whichisthemethodofimprovingthegeneralizationbyconstrainingthesizeofthenetworkweights.Mackay[14]discussedapracticalBayesianframeworkforback-propagationnetworks,whichconsistentlyproducednetworkswithgoodgeneralization.

Theinitialobjectiveofthetrainingprocessistomini-mizethesumofsquareerrors:

（1）

Where

arethetargetsand

aretheneuralnetworkresponsestotherespectivetargets.Typically,trainingaimstoreducethesumofsquarederrorsF=Ed.However,regularizationaddsanadditionalterm,theobjectivefunction,

（2）

Inequation

（2）,

isthesumofsquaresofthenetworkweights,andαandβareobjectivefunctionparameters.Therelativesizeoftheobjectivefunctionparametersdictatestheemphasisfortraining.Ifα<<β,thenthetrainingalgorithmwilldrivetheerrorssmaller.Ifα>>β,trainingwillemphasizeweightsizereductionattheexpenseofnetworkerrors,thusproducingasmoothernetworkresponse[15].

TheBayesianSchoolofstatisticsisbasedonadifferentviewofwhatitmeanstolearnfromdata,inwhichprobabilityisusedtorepresenttheuncertaintyabouttherelationshipbeinglearned.Beforeseeinganydata,theprioropinionsaboutwhatthetruerelationshipmightbecanbeexpressedinaprobabilitydistributionoverthenetworkweightsthatdefinethisrelationship.Aftertheprogramconceivesthedata,therevisedopinionsarecapturedbyaposteriordistributionovernetworkweights.Networkweightsthatseemedplausiblebefore,butwhichdonotmatchthedataverywell,willnowbeseenasbeingmuchlesslikely,while

theprobabilityforvaluesoftheweightsthatdofitthedatawellwillhaveincreased[16].

IntheBayesianframeworktheweightsofthenetworkareconsideredrandomvariables.Afterthedataistaken,theposteriorprobabilityfunctionfortheweightscanbeupdatedaccordingtoBayes’rule:

（3）

Inequation（3）,Drepresentsthedataset,Mistheparticularneuralnetworkmodelused,andwisthevectorofnetworkweights.

isthepriorprobability,whichrepresentsourknowledgeoftheweightsbeforeanydataiscollected.

isthelikelihoodfunction,whichistheprobabilityofdataoccurring,giventheweightsw.

isanormalizationfactor,whichguaranteesthatthetotalprobabilityis1[15].

Inthisstudy,weemployedtheMATLABNeuralNet-worksToolboxfunction“trainbr”whichisanincorporationoftheLevenberg–MarqaurdtalgorithmandtheBayesianregularizationtheorem（orBayesianlearning）intoback-