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：RadiallyorientedNd–Fe–BringmagnetswerepreparedbybackwardextrusionofMQ-Cpowder.Thepunchchamferradiushasagreatimpactonthemicrostructureandmagneticpropertiesoftheringmagnet.Withthechamferradiuschangingfrom2,5to8mm,thecracksintheinnerwalldecreaseobviouslywhilethecrystallographicalignmentdrops.Furthermore,themechanismofcaxisgrowthwassuggestedtobeacombinationofsheardeformationinthecornerandsolution-precipitationunderthestressparalleltoradialdirection.Thealignmentdropsonthetopofringbecausethegrainsgrowfreelyandsometexturedgrainsgrowthroughnucleationandrecrystallization.Inthepresentwork,theoptimalpunchchamferradiusisfoundtobe2mm,andinthiscase,theremanence,coercivity,andmaximumenergyproductoftheringmagnetachieve1.4T,670kJám,and342kJám,respectively.

简介：TooptimizethemagneticpropertiesofnanocompositeNd9Fe85B6magnets,theas-quenchedribbonswithdifferentmicrostructureswerepreparedatsixwheelvelocitiesfrom10to30ms-1throughrapidquenching,followedbyaseriesofannealingtreatmentsat550–800°Cfor5–10min.Itisfoundthatboththelargeinitialgrainsatlowcoolingrateandhighcontentofamorphousphaseathighcoolingratecausea-Fegrainscoarsening,whichleadstoadeclineinthestrengthofexchangecouplinginteractionandthedeteriorationofmagneticproperties.Inordertooptimizethemagneticproperties,theas-quenchedribbonsshouldbechosenwithrelativelysmallinitialgrainsaswellasasmallamountofamorphousphase.FornanocompositeNd9Fe85B6materials,theoptimizedmagneticpropertiesofHcj=446kAm-1,Br=0.86T,(BH)max=80kJm-3areobtainedforribbonspreparedat18ms-1afterannealingat620°Cfor5min.

简介：为改善La-Mg-Ni系A2B7型合金的电化学贮氢性能，在合金中添加一定量的Si元素，通过真空熔炼及退火处理的方法制备La0.8Mg0.2Ni3.3Co0.2Six（x=0-0.2）电极合金。研究Si元素的添加对合金结构及电化学贮氢性能的影响。结果表明，铸态及退火态合金均为多相结构，分别为Ce2Ni7型的（La,Mg）2Ni7相和CaCu5型的LaNi5相以及少量的残余相LaNi3。Si元素的添加没有改变合金的主相，但使得合金中的（La,Mg）2Ni7相减少而LaNi5相增加。添加Si显著地影响了合金的电化学性能。随着Si含量的增加，铸态及退火态合金的放电容量逐步降低，但循环稳定性却随着Si含量的增加而增强。此外，合金电极的高倍率放电性能、极限电流密度、氢扩散系数以及电化学交流阻抗谱的测试均表明合金的电化学动力学性能随着Si含量的增加先增加而后减小。

简介：研究添加Al-5Ti-lB-RE细化剂对Al-7．0Si-0．55Mg（A357）合金的显微组织和力学性能的影响。先利用真空熔炼技术制各Al-7．0Si-0．55Mg合金，然后在Al-7．0Si-0．55Mg合金中加入不同成分的Al-5Ti-1B-RE中间合金。通过X射线衍射仪（XRD）、金相显微镜（OM）和扫描电子显微镜（SEM）对显微组织和拉伸试样的断口形貌进行观察。在室温下对合金的力学性能进行测试。观察Al-5Ti-1B-RE细化剂的形态以及内部结构，可以发现以TiB，为异质形核核心的TiAl3／Ti2Al20RE的壳层结构相。在Al-7．0Si-0．55Mg合金中加入Al-5Ti-1B-3．0RE细化剂后，抗拉强度会有明显提升，直到0．2％添加量时，抗拉强度会达到峰值。