面向切削用量的刀具壽命預(yù)測(cè)及刀具壽命管理系統(tǒng)的研發(fā).pdf

1、重慶大學(xué)碩士學(xué)位論文面向切削用量的刀具壽命預(yù)測(cè)及刀具壽命管理系統(tǒng)的研發(fā)姓名：葉蔚申請(qǐng)學(xué)位級(jí)別：碩士專(zhuān)業(yè)：機(jī)械制造及其自動(dòng)化指導(dǎo)教師：王時(shí)龍20090601重慶大學(xué)碩士學(xué)位論文英文摘要ABSTRACTToollifeisanimportantparameterintheHighSpeedMachiningItdirectlyaffectstheestablishmentofrequirementsplanningofcuttingtool

2、s，adjustaccountsoftools’costandthedrawingupofcuringparametersFuthermore，itaffectsproductionefficiencyandprocessingcostHoweverthetoollifedepends011thetoolwareandisdefinedastheusabletimebeforetheflankweal“hasreachedthecrit

3、erionvalueBecausethetoollifeisinfluencedbymanyfactors，itisnotonlycomplicatedandhardtogettheappropriateresults，butalsoitisverydifficultintheprocessofnewmaterialsandnewtechniqueifitiscalculatedbytraditionalmethodTherefore，

4、predictingtoolwarerapidlyandappropriatelyisveryimportantintheHigh—SpeedMachiningAfteranalyzingthedefectsinthetraditionalcalculatingmethodsoftoollife，oneofthetechnologiesofartificialintelligenceBPNeuralNetworkwasintroduce

5、dInthetechnologythemodelsoftoollifepredictingandcuringparametersoptimizationwereestablishedThroughcontrastedwiththetraditionalcalculatingmethods，itwasprovedthatitcouldreflectexactlythevaryingrelationsbetweenthephysicsqua

6、ntitiesandtheeffectfactorsthroughsimplymodelbuildingandefficientlynetworkstudyingHoweverbecausethegoalofthealgorithmistoseekprecisionofsamples，itwillleadtotheOVerfiringandlowgeneralizationcapabilityLeastsquaressupportvec

7、tormachinesWasintroducedtobuildthemodelforpredictingthetoolWareBecausethemethodbasedontheStructuralRiskMinimizationrule，theweakofBPNeuralNetworkCanbeovercomeThroughlearningthesampleswhichwerecollectedfromtheexperiments，a

8、modelWasbuiltandsimulatedbyMatlabTheresultsofRMSEvalueswerebetween002and003ItprovedthatthealgorithmhadabeRereffectthroughapracticalsampleAfteranalyzingthesecurityandthedatabackupofthecurrentmanagementsystemweproposetoenc