耳机塑料模具设计【18张CAD图纸和说明书】
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编号:10106406
类型:共享资源
大小:8.62MB
格式:RAR
上传时间:2018-05-15
上传人:俊****计
认证信息
个人认证
束**(实名认证)
江苏
IP属地:江苏
40
积分
- 关 键 词:
-
耳机
塑料
模具设计
18
cad
图纸
以及
说明书
仿单
- 资源描述:
-
摘要
近年来,工程塑料以其优异的性能获得了越来越广泛的应用。而注塑模具是
其中发展较快的种类,在人们生活的各个领域都能够见到塑料制品。因此,研究
注塑模具对了解塑料产品的生产过程和提高产品质量有很大意义。
本设计分析了耳机的结构,提出了模具设计的关键点,设计了模具的整体结
构。根据塑件分型面的位置,设计了斜导柱外侧抽芯结构,零件采用了单分型面
的点浇口,提高了零件的外面质量。通过对塑件进行工艺的分析及其结构分析,
从产品结构工艺性,具体模具结构出发,对模具的浇注系统、模具成型部分的结
构、顶出系统、注射机的选择及有关参数的校核都有详细的设计。该模具一模四
腔,采用顶针顶出结构。经过生产验证,该模具结构合理,动作可靠。
关键词:耳机;塑料模具;注射机





- 内容简介:
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INTJADVMANUFTECHNOL2001172973042001SPRINGERVERLAGLONDONLIMITEDOPTIMUMGATEDESIGNOFFREEFORMINJECTIONMOULDUSINGTHEABDUCTIVENETWORKJCLINDEPARTMENTOFMECHANICALDESIGNENGINEERING,NATIONALHUWEIINSTITUTEOFTECHNOLOGY,YUNLIN,TAIWANTHISSTUDYUSESTHEINJECTIONPOSITIONANDSIZEOFTHEGATEASTHEMAJORCONTROLPARAMETERSFORASIMULATEDINJECTIONMOULDONCETHEINJECTIONPARAMETERSGATESIZEANDGATEPOSITIONAREGIVEN,THEPRODUCTPERFORMANCEDEFORMATIONCANBEACCURATELYPREDICTEDBYTHEABDUCTIVENETWORKDEVELOPEDTOAVOIDTHENUMEROUSINFLUENCINGFACTORS,FIRSTTHEPARTLINEOFTHEPARAMETEREQUATIONISCREATEDBYANABDUCTIVENETWORKTOLIMITTHERANGEOFTHEGATETHEOPTIMALINJECTIONPARAMETERSCANBESEARCHEDFORBYASIMULATIONANNEALINGSAOPTIMISATIONALGORITHM,WITHAPERFORMANCEINDEX,TOOBTAINAPERFECTPARTTHEMAJORPURPOSEISSEARCHINGFORTHEOPTIMALGATELOCATIONONTHEPARTSURFACE,ANDMINIMISINGTHEAIRTRAPANDDEFORMATIONAFTERPARTFORMATIONTHISSTUDYALSOUSESAPRACTICALEXAMPLEWHICHHASBEENANDPROVEDBYEXPERIMENTTOACHIEVEASATISFACTORYRESULTKEYWORDSABDUCTIVENETWORKINJECTIONMOULDSIMULATIONANNEALINGSA1INTRODUCTIONOWINGTOTHERAPIDDEVELOPMENTOFINDUSTRYANDCOMMERCEINRECENTYEARS,THEREISANEEDFORRAPIDANDHIGHVOLUMEPRODUCTIONOFGOODSTHEPRODUCTSAREMANUFACTUREDUSINGMOULDSINORDERTOSAVETHETIMEANDCOSTPLASTICPRODUCTSARETHEMAJORITYOWINGTOTHESEPRODUCTSNOTREQUIRINGCOMPLICATEDPROCESSESITISPOSSIBLETOCOPEWITHMARKETDEMANDSPEEDILYANDCONVENIENTLYINTRADITIONALPLASTICPRODUCTION,THEDESIGNSOFTHEPORTIONSOFTHEMOULDAREDETERMINEDBYHUMANSHOWEVER,BECAUSEOFTHEINCREASEDPERFORMANCEREQUIREMENTS,THECOMPLEXITYOFPLASTICPRODUCTSHASINCREASEDFIRST,THEGEOMETRICSHAPESOFTHEPLASTICPRODUCTSAREDIFFICULTTODRAW,ANDTHEINTERNALSHAPEISOFTENCOMPLEXWHICHALSOAFFECTSTHEPRODUCTIONOFTHEPRODUCTINJECTIONPROCESSINGCANBEDIVIDEDINTOTHREESTAGESCORRESPONDENCEANDOFFPRINTREQUESTSTODRJCLIN,DEPARTMENTOFMECHANICALDESIGNENGINEERING,NATIONALHUWEIINSTITUTEOFTECHNOLOGY,YUNLIN632,TAIWANEMAILLINRCKSUNWSNHITEDUTW1HEATTHEPLASTICMATERIALTOAMOLTENSTATETHEN,BYHIGHPRESSURE,FORCETHEMATERIALTOTHERUNNER,ANDTHENINTOTHEMOULDCAVITY2WHENTHEFILLINGOFTHEMOULDCAVITYISCOMPLETED,MOREMOLTENPLASTICSHOULDBEDELIVEREDINTOTHECAVITYATHIGHPRESSURETOCOMPENSATEFORTHESHRINKAGEOFTHEPLASTICTHISENSURESCOMPLETEFILLINGOFTHEMOULDCAVITY3TAKEOUTTHEPRODUCTAFTERCOOLINGTHOUGHTHEFILLINGPROCESSISONLYASMALLPROPORTIONOFTHECOMPLETEFORMATIONCYCLE,ITISVERYIMPORTANTIFFILLINGININCOMPLETE,THEREISNOPRESSUREHOLDINGANDCOOLINGISREQUIREDTHUS,THEFLOWOFTHEPLASTICFLUIDSHOULDBECONTROLLEDTHOROUGHLYTOENSURETHEQUALITYOFTHEPRODUCTTHEISOTHERMALFILLINGMODELOFANEWTONIANFLUIDISTHESIMPLESTINJECTIONMOULDFLOWFILLINGMODELRICHARDSON1PRODUCEDACOMPLETEANDDETAILEDCONCEPTTHEMAJORCONCEPTISBASEDONTHEAPPLICATIONOFLUBRICATIONTHEORY,ANDHESIMPLIFIEDTHECOMPLEX3DFLOWTHEORYTO2DHELESHAWFLOWTHEHELESHAWFLOWWASUSEDTOSIMULATETHEPOTENTIALFLOWANDWASFURTHERMOREUSEDINTHEPLASTICITYFLOWOFTHEPLASTICHEASSUMEDTHEPLASTICITYFLOWONANEXTREMELYTHINPLATEANDFULLYDEVELOPEDTHEFLOWBYIGNORINGTHESPEEDCHANGETHROUGHTHETHICKNESSKAMALETALUSEDSIMILARMETHODSTOOBTAINTHEFILLINGCONDITIONFORARECTANGULARMOULDCAVITY,ANDTHEANALYTICALRESULTOBTAINEDWASALMOSTIDENTICALTOTHEEXPERIMENTALRESULTPLASTICMATERIALFOLLOWSTHENEWTONIANFLUIDMODELFORFLOWINAMOULDCAVITY,ANDBIRDETAL24DERIVEDMOULDFLOWTHEORYBASEDONTHISWHENTHESHAPEOFAMOULDISCOMPLICATEDANDTHEREISVARIATIONINTHICKNESS,THENTHEEQUILIBRIUMEQUATIONSCHANGESTONONLINEARANDHASNOANALYTICALSOLUTIONTHUS,ITCANBESOLVEDONLYBYFINITEDIFFERENCEORNUMERICALSOLUTIONS2,5OFCOURSE,ASTHEPOLYMERISAVISCOELASTICFLUID,ITISBESTTOSOLVETHEFLOWPROBLEMBYUSINGVISCOELASTICITYEQUATIONSIN1998,GOYALETALUSEDTHEWHITEMETZNERVISCOELASTICITYMODELTOSIMULATETHEDISKMOULDFLOWMODELFORCENTRALPOURINGMETZNER,USINGAFINITEDIFFERENCEMETHODTOSOLVETHEGOVERNINGEQUATION,FOULDTHEVISCOELASTICITYEFFECTWOULDNOTCHANGETHEDISTRIBUTIONOFSPEEDANDTEMPERATUREHOWEVER,ITAFFECTSTHESTRESSFIELDVERYMUCHIFITISAPUREVISCOELASTIC298JCLINFLOWMODEL,THEPOPULARGNFMODELISGENERALLYUSEDTOPERFORMNUMERICALSIMULATIONCURRENTLY,FINITEELEMENTMETHODSAREMOSTLYUSEDFORTHESOLUTIONOFMOULDFLOWPROBLEMSOTHERMETHODSAREPUREVISCOELASTICMODELS,SUCHASCFOLWANDMOLDFLOWSOFTWAREWEUSEDTHISMETHODASWELLSOMESOFTWAREEMPLOYSTHEVISCOELASTICWHITEMETZNERMODEL,BUTITISLIMITEDTO2DMOULDFLOWANALYSISSIMPLEMOULDFLOWANALYSISISLIMITEDBYCPUTIMEFORTHECOMPLICATEDMOULDSHAPES,PAPTHANASIONETALUSEDUCMFLUIDFORFILLINGANALYSIS,USINGAFINITEDIFFERENCEMETHODANDBFCCCOORDINATIONSYSTEMAPPLICATIONFORTHESOLUTIONOFTHEMORECOMPLICATEDMOULDSHAPEANDFILLINGPROBLEM,BUTITWASNOTCOMMERCIALISED6MANYFACTORSAFFECTPLASTICMATERIALINJECTIONTHEFILLINGSPEED,INJECTIONPRESSUREANDMOLTENTEMPERATURE,HOLDINGPRESSURE712,COOLINGTUBE13,14ANDINJECTIONGATEAFFECTTHEACCURACYOFTHEPLASTICPRODUCT,BECAUSE,WHENTHEINJECTIONPROCESSINGISCOMPLETED,THEFLOWOFMATERIALINTHEMOULDCAVITYRESULTSINUNEVENTEMPERATUREANDPRESSURE,ANDINDUCESRESIDUALSTRESSANDDEFORMATIONOFTHEWORKPIECEAFTERCOOLINGITISDIFFICULTTODECIDEONTHEMOULDPARTSURFACEANDGATEPOSITIONSGENERALLY,THEMOULDPARTSURFACEISLOCATEDATTHEWIDESTPLANEOFTHEMOULDSEARCHINGFORTHEOPTIMALGATEPOSITIONDEPENDSONEXPERIENCEMINIMALMODIFICATIONTOTHEMOULDISREQUIREDIFYOUARELUCKYHOWEVER,THETIMEANDCOSTREQUIREDFORTHEMODIFICATIONOFMOSTINJE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HISSTUDYUSESANACTUALINDUSTRIALPRODUCTASASAMPLE,FIG1THEMOULDPARTSURFACEISLOCATEDATTHEMAXIMUMPROJECTIONAREAASSHOWNINFIG1,THEBOTTOMISTHEWIDESTPLANEANDISCHOSENASTHEMOULDPARTSURFACEHOWEVER,MOSTIMPORTANTISTHESEARCHINGOFGATEPOSITIONONTHEPARTSURFACETHISSTUDYESTABLISHESTHEPARAMETEREQUATIONBYUSINGANABDUCTIVENEURONNETWORK,INORDERTOESTABLISHTHESIMULATEDANNEALINGMETHODSATOFINDTHEOPTIMALGATEPATHPOSITIONTHEPARAMETEREQUATIONOFAPARTSURFACEISEXPRESSEDBYFYXFIRST,USEACMMSYSTEMTOMEASURETHEXYZCOORDINATEVALUESINTHISSTUDYZ0OF22POINTSONTHEMOULDPARTLINEONTHEMOULDPARTSURFACEASILLUSTRATEDINTABLE1,ANDTHEGATEPOSITIONISCOMPLETELYONTHECURVEINTHISSPACEPRIORTODEVELOPINGASPACECURVEMODEL,ADATABASEHASTOBETRAINED,ANDAGOODRELATIONSHIPMSUTEXISTBETWEENTHECONTROLPOINTANDABDUCTIVENETWORKSYSTEMACORRECTANDFIG1INJECTIONMOULDPRODUCT300JCLINTABLE1X,YCOORDINATESETNUMBERXCOORDINATEYCOORDINATE1002462163433332835452920457310566934097113323581298394913855571014127341113699671212961191310002103149332316158642528167982739177872831187802929197833034207603130217073215226113249PRECISECURVEEQISHELPFULFORFINDINGTHEOPTIMALGATELOCATIONTOBUILDACOMPLETEABDUCTIVENETWORK,THEFIRSTREQUIREMENTISTOTRAINTHEDATABASETHEINFORMATIONGIVENBYTHEINPUTANDOUTPUTPARAMETERSMUSTBESUFFICIENTAPREDICTEDSQUAREERRORPSECRITERIONISTHENUSEDTODETERMINEAUTOMATICALLYANOPTIMALSTRUCTURE23THEPSECRITERIONISUSEDTOSELECTTHELEASTCOMPLEXBUTSTILLACCURATENETWORKTHEPSEISCOMPOSEDOFTWOTERMSPSEFSEKP16WHEREFSEISTHEAVERAGESQUAREERROROFTHENETWORKFORFITTINGTHETRAININGDATAANDKPISTHECOMPLEXPENALTYOFTHENETWORK,SHOWNBYTHEFOLLOWINGEQUATIONKPCPM2S2PKN17WHERECPMISTHECOMPLEXPENALTYMULTIPLIER,KPISACOEFFICIENTOFTHENETWORKNISTHENUMBEROFTRAININGDATATOBEUSEDANDS2PISAPRIORESTIMATEOFTHEMODELERRORVARIANCEBASEDONTHEDEVELOPMENTOFTHEDATABASEANDTHEPREDICTIONOFTHEACCURACYOFTHEPARTSURFACE,ATHREELAYERABDUCTIVENETWORK,WHICHCOMPRISEDDESIGNFACTORSINPUTVARIOUSYCOORDINATEANDOUTPUTFACTORSXCOORDINATEISSYNTHESISEDAUTOMATICALLYITISCAPABLEOFPREDICTINGACCURATELYTHESPACECURVEATANYPOINTUNDERVARIOUSCONTROLPARAMETERSALLPOLYNOMIALEQUATIONSUSEDINTHISNETWORKARELISTEDINAPPENDIXAPSE58103TABLE2COMPARESTHEERRORPREDICTEDBYTHEABDUCTIVEMODELANDCMMMEASUREMENTDATATHEMEASUREMENTDAAISEXCLUDEDFROMTHE22SETSOFCMMMEASUREMENTDATAFORESTABLISHINGTHEMODELTHISSETOFDATAISUSEDTOTESTTHEAPPROPRIATENESSOFTHEMODELESTABLISHEDABOVEWECANSEEFROMTABLE2THATTHEERRORISAPPROXIMATELY213,WHICHSHOWSTHATTHEMODELISSUITABLEFORTHISSPACECURVETABLE2CMMSCOORDINATEANDNEURALNETWORKPREDICTCOMPAREDITISNOTINCLUDEDINANYORIGINAL22SETSDATABASEITEMSCMMSNEURALNETWORKERRORVALUESCOORDINATEPREDICTCMMSPREDICT/COORDINATECMMSCOORDINATE1125,1601101,1602135CREATETHEINJECTIONMOULDMODELSIMILARLY,THERELATIONSHIPISESTABLISHEDBETWEENINPUTPARAMETERSGATELOCATIONANDGATESIZEANDTHEOUTPUTPARAMETERDEFORMATIONDURINGTHEINJECTIONPROCESSTOBUILDACOMPLETEABDUCTIVENETWORK,THEFIRSTREQUIREMENTISTOTRAINTHEDATABASETHEINFORMATIONGIVENBYTHEINPUTANDTHEOUTPUTDATAMUSTBESUFFICIENTTHUS,THETRAININGFACTORGATELOCATIONFORABDUCTIVENETWORKTRAININGSHOULDBESATISFACTORYFORMAKINGDEFECTFREEPRODUCTSFIGURE2SHOWSTHESIMULATIONOFFEMMOULDFLOWTABLE3SHOWSTHEPOSITIONOFTHEGATEANDTHEMAXIMUMDEFORMATIONOFTHEPRODUCTOBTAINEDFROMMOULDFLOWANALYSISBASEDONTHEDEVELOPMENTOFTHEINJECTIONMOULDMODEL,THREELAYERABDUCTIVENETWORKS,WHICHARECOMPRISEDOFINJECTIONMOULDCONDITIONSANDTHEINJECTIONRESULTSDEFORMATION,ARESYNTHESISEDAUTOMATICALLYTHEYARECAPABLEOFPREDICTINGACCURATELYTHEPRODUCTSTRAINTHERESULTOFINJECTIONMOULDEDPRODUCTUNDERVARIOUSCONTROLPARAMETERSALLPOLYNOMIALEQUATIONSUSEDINTHISNETWORKARELISTEDINAPPENDIXBPSE23105TABLE4COMPARESTHEERRORPREDICTEDBYTHEABDUCTIVEMODELANDTHESIMULATIONCASETHESIMULATIONCASEISEXCLUDEDFROMTHE22SETSOFSIMULATIONCASESFORESTABLISHINGTHEMODELTHISSETOFDATAISUSEDTOTESTTHEAPPROPRIATENESSOFTHEMODELESTABLISHEDABOVEWECANSEEFROMTABLE4THATTHEERRORISFIG2THEDEFORMATIONOFFEMMOULDFLOWOPTIMUMGATEDESIGNOFFREEFORMINJECTIONMOULD301TABLE3GATELOCATIONANDTHEMAXIMUMSTRAINRELATIONSHIPSETNUMBERXCOORDINATEYCOORDINATEGATEWIDTHGATELENGTHPRODUCEMAXSTRAIN100246052511475034821634330715303153332835087519125027104529204105229502858573105605251147503017693409071530526711332350875191250236981298394105229502517913855570525114750278810141273407153027731113699670875191250298812129611910522950299713100021030525114750257614933231607153026241586425280875191250254216798273910522950249517787283105251147502503187802929071530245619783303408751912502596207603130105229502457217073215052511475024992261132490715302511TABLE4MOULDFLOWSIMULATEDANDNEURALNETWORKPREDICTCOMPAREDITISNOTINCLUDEDINANYORIGINAL22SETDATABASEITEMSFEMMOULDFLOWNEURALNETWORKSIMULATIONPREDICTXCOORDINATE11011101YCOORDINATE160160GATEWIDTH1818GATEHEIGHT0909PRODUCEMAXDEFORMATION0317803325ERRORVALUES462FEMPREDICT/FEMAPPROXIMATELY462,WHICHSHOWSTHATTHEMODELISSUITABLEFORTHISMODELREQUIREMENT6SIMULATIONANNEALINGTHEORYIN1983,ATHEORYTHATWASCAPABLEOFSOLVINGTHEGLOBALOPTIMISATIONPROBLEMWASDEVELOPEDFORTHEOPTIMISEDPROBLEMTHECONCEPTWASAPOWERFULOPTIMISATIONALGORITHMBASEDONTHEANNEALINGOFASOLIDWHICHSOLVEDTHECOMBINATORIALOPTIMISATIONPROBLEMOFMULTIPLEVARIABLESWHENTHETEMPERA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