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基于BP神经网络和遗传算法的机箱壳注塑工艺参数多目标优化.docx

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基于BP神经网络和遗传算法的机箱壳注塑工艺参数多目标优化 Abstract: Inthispaper,weproposeamulti-objectiveoptimizationmodelbasedonBPneuralnetworkandgeneticalgorithmforinjectionmoldingprocessparametersofchassisshells.Ourgoalistoimprovethequalityofinjectionmoldedproductswhilereducingproductioncosts.Theproposedmodelisbasedontheprincipleofmachinelearningandgeneticalgorithm,whichcanautomaticallyadjusttheapplicableparametersduringtheinjectionmoldingprocess,suchasinjectionpressure,injectionspeed,andmoldtemperature,toachievethebestresults.Weconductedexperimentsusingreal-worlddatasetsandtheresultsdemonstratedthattheproposedmodeliseffectiveinimprovingproductqualityandreducingproductioncosts. Introduction: Injectionmoldingisawidelyusedmanufacturingprocessforproducingplasticproducts.Thequalityofinjectionmoldedproductsislargelydeterminedbytheprocessparametersofinjectionmolding,suchasinjectionpressure,injectionspeed,moldtemperature,etc.Optimizationofinjectionmoldingprocessesisessentialforbusinessestoachievehigh-qualityproductsatlowerproductioncosts.Inthispaper,weproposeamulti-objectiveoptimizationmodelbasedonBPneuralnetworkandgeneticalgorithmtooptimizetheinjectionmoldingprocessparametersofchassisshells. Background: Chassisshellsareanimportantcomponentofvariouselectronicproducts.Theproductionofthesepartsinvolvescomplexinjectionmoldingprocesses.Thequalityofthefinishedproductisinfluencedbynumerousfactors,suchasmaterialproperties,molddesign,theflowofmoltenplastic,andprocessingparameters.Improvingthequalityofthesepartsrequirestheoptimizationofinjectionmoldingprocessparameters. Methodology: Theproposedmodelconsistsoftwomaincomponents;BPneuralnetworkandgeneticalgorithm.TheBPneuralnetworkisusedtopredictthequalityindicesofthemoldedparts,includingwarpage,shrinkage,andmechanicalproperties.Thesequalityindicesareusedasobjectivefunctionsinmulti-objectiveoptimization.Thegeneticalgorithmisusedtooptimizetheinjectionmoldingprocessparametersandsearchfortheoptimalsolutionthatachievesthedesiredqualityindiceswithminimalproductionc