预览加载中，请您耐心等待几秒...

一种数控机床信息感知与状态识别方法.docx

预览
1/3
2/3
3/3

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

一种数控机床信息感知与状态识别方法 Title:ANovelMethodforSensingandRecognizingInformationandStatesinNumericalControlMachineTools Introduction: Thesignificantadvancementsintechnologyhavebroughtforththeageofautomation,particularlyinindustriessuchasmanufacturing.Inthefieldofmanufacturing,numericalcontrol(NC)machinetoolsplayacrucialroleinachievingefficiencyandprecision.However,ensuringtheoptimalperformanceofthesemachinesrequirescontinuousmonitoringoftheirinformationandstates.Thispaperpresentsanovelmethodforsensingandrecognizinginformationandstatesinnumericalcontrolmachinetools,enablingreal-timemonitoringanddecision-makingtoimproveproductivityandreducedowntime. 1.Background: Numericalcontrolmachinetoolsareextensivelyusedinmanufacturingprocessesduetotheirabilitytoexecutecomplexoperationswithhighaccuracy.Thesemachinetoolsconsistofvarioussensors,controllers,andactuatorsthatinteractwiththeworkpiecetoachievethedesiredmachiningoperations.Effectivelymonitoringtheinformationandstatesofthesemachinetoolsisvitalforoptimizingtheirperformance,detectinganomalies,andpreventingequipmentfailures. 2.InformationSensing: Thefirststepintheproposedmethodisinformationsensing.Thisinvolvescapturingdatafrommultiplesources,includingsensorsembeddedwithinthemachinetoolandexternalsourcessuchasexternalmetrologydevices.Sensorssuchasaccelerometers,encoders,andforcesensorscanprovidemeasurementsrelatedtovibration,position,andcuttingforces,respectively.Additionally,externalmetrologydevicescanbeusedtomeasuredimensionalaccuracyandsurfacefinish.Dataacquisitionsystemsefficientlycollectthisinformationandstoreitforfurtheranalysis. 3.DataProcessing: Oncetheinformationiscollected,itundergoesacomprehensivedataprocessingstage.Rawdatafromvarioussensorsaretransformed,cleaned,andsynchronizedtoensurecompatibilityandconsistency.Thisstagealsoinvolvesdatafusiontechniquestomergemultiplesourcesofinformationintoasinglecoherentrepresentationofthemachine'sstate.MachinelearningalgorithmssuchasPrincipalComponentAnalysis(PCA)canbeappliedtoextractre