四輥冷軋機(jī)軋制過程工作輥溫度場及熱變形預(yù)測控制研究摘要：本文針對四輥冷軋機(jī)軋制過程中工作輥溫度場變化和熱變形的預(yù)測控制問題展開了研究。針對工作輥溫度場變化的復(fù)雜性，本文采用了多物理場耦合模型，考慮了板材溫度場、滾制接觸變形、輥筒轉(zhuǎn)動、輥殼傳熱等因素，并采用有限元方法對模型進(jìn)行了求解，得到了工作輥溫度變化規(guī)律。同時，本文建立了基于BP神經(jīng)網(wǎng)絡(luò)的熱變形預(yù)測模型，通過對軋制實(shí)驗(yàn)數(shù)據(jù)的建模，得到了預(yù)測模型，并對模型進(jìn)行了仿真驗(yàn)證。最后，本文基于預(yù)測模型設(shè)計(jì)了預(yù)測控制算法，并進(jìn)行了仿真模擬。仿真結(jié)果表明，本文所提出的預(yù)測控制算法能夠有效地控制軋制過程中的溫度變化和熱變形，提高了軋制品質(zhì)和生產(chǎn)效率。

關(guān)鍵詞：四輥冷軋機(jī)；工作輥溫度場；熱變形預(yù)測控制；多物理場耦合模型；BP神經(jīng)網(wǎng)絡(luò)；仿真模擬

Abstract:Thispaperfocusesonthestudyofthetemperaturefieldvariationoftheworkingrollsandthepredictionandcontrolofthermaldeformationintheprocessofcoldrollingonafour-rollmill.Consideringthecomplexityofthetemperaturefieldchangeoftheworkingrolls,amultiphysicscouplingmodelisadoptedinthispaper,whichtakesintoaccountthetemperaturefieldoftheplate,therollingcontactdeformation,therotationoftherollers,theheattransferoftherollershellandotherfactors.Thefiniteelementmethodisusedtosolvethemodel,andthetemperaturechangeoftheworkingrollsisobtained.Atthesametime,athermaldeformationpredictionmodelbasedonBPneuralnetworkisestablishedinthispaper.Bymodelingtherollingexperimentaldata,thepredictionmodelisobtainedandverifiedbysimulation.Finally,basedonthepredictionmodel,thepredictionandcontrolalgorithmisdesignedandsimulated.Thesimulationresultsshowthattheproposedpredictionandcontrolalgorithmcaneffectivelycontrolthetemperaturechangeandthermaldeformationintherollingprocess,andimprovethequalityandproductionefficiencyoftherollingproducts.

Keywords:four-rollmill;workingrolltemperaturefield;thermaldeformationpredictionandcontrol;multiphysicscouplingmodel;BPneuralnetwork;simulationandmodeling。Intherollingprocess,theworkingrolltemperaturefieldandthermaldeformationareimportantfactorsthataffectthequalityandproductionefficiencyoftherollingproducts.Toeffectivelycontrolthesefactors,amultiphysicscouplingmodelwasproposedtodescribetherollingprocess,whichtakesintoaccounttheheattransfer,deformation,andstressdistributioninthefour-rollmill.

Basedonthemultiphysicscouplingmodel,aBPneuralnetworkwastrainedtopredictthetemperaturechangeandthermaldeformationduringtherollingprocess.Thetrainingdataweregeneratedbysimulatingtherollingprocessunderdifferentrollingspeeds,rollingforces,andinitialtemperatures.

Usingthepredictedtemperatureanddeformationdata,acontrolalgorithmwasdevelopedtoadjusttherollingparametersinreal-time,suchastherollingspeed,rollingforce,andcoolantflowrate.Thecontrolalgorithmwasdesignedtominimizethetemperaturechangeandthermaldeformationintheworkingroll,whilemaintainingthedesiredproductqualityandproductionefficiency.

Tovalidatetheproposedpredictionandcontrolalgorithm,simulationswereconductedunderdifferentrollingconditions.Thesimulationresultsshowedthatthealgorithmeffectivelycontrolledthetemperaturechangeandthermaldeformationintheworkingroll,andimprovedthequalityandproductionefficiencyoftherollingproducts.

Overall,theproposedmultiphysicscouplingmodel,BPneuralnetwork,andcontrolalgorithmprovideapromisingapproachforimprovingtheprecisionandefficiencyoftherollingprocess.Furtherresearchisneededtovalidatethealgorithminreal-worldrollingapplicationsandtooptimizethealgorithmfordifferentrollingmaterialsandproductspecifications。Inadditiontotheproposedapproach,thereareothermethodsbeingdevelopedtoimprovetherollingprocess.Onesuchmethodistheuseofadvancedmaterialmodelstosimulatetherollingprocessandpredictthebehaviorofthematerial.Thesemodelsconsiderthemicrostructureofthematerial,theinteractionsbetweenthematerialandtherolls,andthethermalconditionsduringtheprocess.

Anotherareaofresearchisthedevelopmentofintelligentcontrolsystemsthatcanmonitorandadjusttherollingprocessinreal-time.Thesesystemsusesensorstocollectdataontherollingprocess,andadvancedalgorithmstoanalyzethedataandmakeadjustmentstotheprocessparameters.

Furthermore,theuseofadvancedsensorsandsystemsformeasuringtheshapeandprofileoftherolledproductisanimportantareaofresearch.Thesesensorscanprovidereal-timefeedbackonthequalityoftheproductandallowforadjustmentstobemadetotherollingprocesstoensurethattheproductmeetsthedesiredspecifications.

Inconclusion,therollingprocessisacomplexandmulti-physicsprocessthatrequirescarefulcontrolandoptimization.Theproposedapproach,whichcombinesamultiphysicscouplingmodel,BPneuralnetwork,andcontrolalgorithm,providesapromisingsolutionforimprovingtheprecisionandefficiencyoftherollingprocess.However,furtherresearchisneededtovalidateandoptimizethealgorithmfordifferentrollingmaterialsandproductspecifications.Moreover,othermethodssuchastheuseofadvancedmaterialmodels,intelligentcontrolsystems,andadvancedsensorsshouldalsobeexploredtoenhancetherollingprocess。Onepotentialareaforfutureresearchinimprovingtherollingprocessistheuseofadvancedmaterialmodels.Thebehaviorofmaterialsduringtherollingprocesscanbecomplexanddifficulttopredict,especiallyforadvancedmaterialssuchascompositesandalloys.Therefore,theuseofadvancedmaterialmodels,suchascrystalplasticityorcontinuumdamagemechanics,couldprovideamoreaccuraterepresentationofthebehaviorofthematerialduringtherollingprocess.Thiscouldleadtomoreprecisecontroloftheprocessandpotentiallyimprovedproductquality.

Intelligentcontrolsystemscouldalsobeexploredasameansofimprovingtheefficiencyandprecisionoftherollingprocess.Thesesystemscouldincorporatedatafromsensorsmonitoringtherollingprocess,aswellasothervariablessuchastemperatureandhumidity,tooptimizetheprocessinreal-time.Byusingmachinelearningalgorithmsandotheradvancedtechniques,thesesystemscouldadapttochangingconditionsandcontinuouslyimprovetherollingprocess.

Finally,theuseofadvancedsensorscouldalsoenhancetherollingprocess.Forexample,advancedimagingtechniquessuchasmicrocomputedtomographycouldbeusedtoprovidedetailedinformationaboutthemicrostructureofthematerialduringtherollingprocess.Thisinformationcouldbeusedtoadjusttheprocessinreal-timeandensureoptimalproductquality.

Inconclusion,therollingprocessplaysacriticalroleintheproductionofawiderangeofproducts,includingmetals,plastics,andcomposites.Avarietyofmethodscanbeusedtoimprovetheprecisionandefficiencyoftheprocess,includingtheuseofmultiphysicsmodels,BPneuralnetworks,andcontrolalgorithms.However,furtherresearchisneededtovalidateandoptimizethesemethods,aswellasexploreotherapproachessuchasadvancedmaterialmodels,intelligentcontrolsystems,andadvancedsensors.Bycontinuingtoadvanceourunderstandingoftherollingprocess,wecanimproveproductquality,reducewaste,andenhancethecompetitivenessofindustriesaroundtheworld。Inadditiontothemethodsmentionedabove,thereareseveralotheravenuesofresearchthatcanbeexploredtoimprovetherollingprocess.Onesuchareaisadvancedmaterialmodels,whichcanhelptobetterpredictthebehaviorofthematerialduringrolling.Thiscanleadtoimprovedprocessdesignandgreatercontroloverthefinalproductquality.

Intelligentcontrolsystemsareanotherareaofresearchthatcouldhaveasignificantimpactontherollingprocess.Byincorporatingmachinelearningalgorithmsandreal-timedataanalysis,thesesystemscanoptimizetherollingprocesson-the-fly,adaptingtochangingconditionsandimprovingefficiencyandproductquality.Additionally,theuseofadvancedsensors,suchastemperatureandstrainsensors,canprovidemoreaccuratedataandfeedbacktocontrolsystems,furtherenhancingtheireffectiveness.

Therearealsoseveralchallengesassociatedwiththerollingprocessthatneedtobeaddressed.Onesuchchallengeistheneedtoreducerollingforceinordertodecreasewearandtearontheequipment,aswellasreduceenergyconsumption.Thiscanbeachievedthroughtheuseoflubricants,suchasoilorwater,aswellasthroughthedevelopmentofnewmaterialsandcoatingsthatreducefriction.

Anotherchallengeistheneedtoimprovetheaccuracyandprecisionoftherollingprocess.Thisisparticularlyimportantinindustriessuchasaerospaceandautomotive,whereevensmalldeviationsinproductdimensionscanhavesignificantconsequences.Toaddressthischallenge,researcherscanexploretheuseofadvancedmetrologytechniques,suchaslaserscanningandmicroscopy,toimprovemeasurementaccuracyandresolution.

Finally,thereisaneedforgreatercollaborationandknowledgesharingbetweenresearchers,industryleaders,andgovernmentagencies.Byworkingtogether,wecanbetterunderstandthechallengesassociatedwiththerollingprocessanddevelopmoreeffectivesolutionsthatimproveproductquality,reducewaste,andenhancecompetitiveness.

Inconclusion,therollingprocessisacritical