简介：Inthepresentstudy,artificialneuralnetwork(ANN)approachwasusedtopredictthestress–straincurveofnearbetatitaniumalloyasafunctionofvolumefractionsofaandb.Thisapproachistodevelopthebestpossiblecombinationorneuralnetwork(NN)topredictthestress–straincurve.Inordertoachievethis,threedifferentNNarchitectures(feed-forwardback-propagationnetwork,cascade-forwardback-propagationnetwork,andlayerrecurrentnetwork),threedifferenttransferfunctions(purelin,Log-Sigmoid,andTan-Sigmoid),numberofhiddenlayers(1and2),numberofneuronsinthehiddenlayer(s),anddifferenttrainingalgorithmswereemployed.ANNtrainingmodules,theloadintermsofstrain,andvolumefractionofaaretheinputsandthestressasanoutput.ANNsystemwastrainedusingthepreparedtrainingset(a,16%a,40%a,andbstress–straincurves).Aftertrainingprocess,testdatawereusedtochecksystemaccuracy.Itisobservedthatfeed-forwardback-propagationnetworkisthefastest,andLog-Sigmoidtransferfunctionisgivingthebestresults.Finally,layerrecurrentNNwithasinglehiddenlayerconsistsof11neurons,andLog-Sigmoidtransferfunctionusingtrainlmastrainingalgorithmisgivinggoodresult,andaveragerelativeerroris1.27±1.45%.Intwohiddenlayers,layerrecurrentNNconsistsof7neuronsineachhiddenlayerwithtrainrpasthetrainingalgorithmhavingthetransferfunctionofLogSigmoidwhichgivesbetterresults.Asaresult,theNNisfoundedsuccessfulforthepredictionofstress–straincurveofnearbtitaniumalloy.

简介：Thenon-linearrelationshipbetweenparametersofrapidlysolidifiedagingprocessesandmechancalandelectricalpropertiesofCu-Cr-Zralloyisavailablebyusingasupervisedartificialneuralnetwork(ANN).Aknowledgerepositoryofrapidlysolidifiedagingprocessesisestablishedviasufficientdatalearningbythenetwork.Thepredictedvaluesoftheneuralnetworkareinaccordancewiththetesteddata.Soaneffectivemeasureforforeseeingandcontrollingthepropertiesoftheprocessingisprovided.