基于神经网络和粒子群算法的冲压成形多目标优化.docx

基于神经网络和粒子群算法的冲压成形多目标优化
基于神经网络和粒子群算法的冲压成形多目标优化
摘要
冲压成形是一种广泛应用于自动化生产线的制造工艺。在冲压生产中，成形质量、生产效率和成本是三个重要的目标。本文提出了一种基于神经网络和粒子群算法的冲压成形多目标优化方法，将神经网络作为代理模型来预测不同工艺参数和目标函数之间的复杂非线性关系，并采用粒子群算法来优化多目标。实验结果表明，该方法可以显著提高成形质量和生产效率，同时降低成本。
关键词：冲压成形；多目标优化；神经网络；粒子群算法
Abstract
Stampingformingisamanufacturingprocesswidelyusedinautomatedproductionlines.Instampingproduction,formingquality,productionefficiencyandcostarethreeimportantobjectives.Inthispaper,weproposeastampingformingmulti-objectiveoptimizationmethodbasedonneuralnetworksandparticleswarmoptimization.Theneuralnetworksareusedassurrogatemodelstopredictthecomplexnonlinearrelationshipsbetweendifferentprocessparametersandobjectivefunctions,andparticleswarmoptimizationisusedtooptimizemultipleobjectives.Experimentalresultsshowthattheproposedmethodcansignificantlyimproveformingqualityandproductionefficiencywhilereducingcost.
Keywords:stampingforming;multi-objectiveoptimization;neuralnetworks;particleswarmoptimization
1.Introduction
Stampingformingisamanufacturingprocesswidelyusedinvariousindustries,suchasautomotive,aerospace,andelectronics.Asacomplexnonlinearmanufacturingprocess,stampingforminginvolvesmultipleprocessparameters,whichhaveasignificantimpactonformingquality,productionefficiencyandcost.Therefore,itiscrucialtooptimizestampingformingprocessparameterstoachievemultipleobjectivessimultaneously.However,thetraditionaltrial-and-errormethodisnotefficientenoughtosolvethemulti-objectiveoptimizationproblem.Therefore,weproposeanovelstampingformingmulti-objectiveoptimizationmethodbasedonneuralnetworksandparticleswarmoptimization.
2.Methodology
Theproposedmethodconsistsoftwoparts:thesurrogatemodelbasedonneuralnetworksandtheoptimizationalgorithmbasedonparticleswarmoptimization.
2.1Surrogatemodelbasedonneuralnetworks
Tobuildasurrogatemodelforthestampingformingprocess,wefirstneedtotrainaneuralnetworkwithasetofinput-outputpairs.Theinputvariablesincludeprocessparameters,suchasblankholderforce,punchspeed,anddieradius,whiletheoutputvariablesincludeobj