Preparation of ZrB_2-SiC Powders via Carbothermal Reduction of Zircon and Prediction of Product Composition by Back-Propagation Artificial Neural Network

Phase pure ZrB_2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO_4,B_2O_3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB_2-SiC was reduced to 1 400℃by the present conditions,and oxide additive(including CoSO_4·7H_2O,Y_2O_3 and TiO_2)was effective in enhancing the decomposition of raw ZrSiO_4,therefore accelerating the synthesis of ZrB_2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB_2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks(BP-ANNs)was adopted to establish a model for predicting the reaction extent(e g,the content of ZrB_2-SiC in final product)in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment(with a mean square error in 10~(-3) degree),verifying good effectiveness of the proposed strategy.     

Preparation of ZrB<Subscript>2</Subscript>-SiC Powders via Carbothermal Reduction of Zircon and Prediction of Product Composition by Back-Propagation Artificial Neural Network

Phase pure ZrB₂-SiC composite powders were prepared after 1 450 °C/3 h via carbothermal reduction route, by using ZrSiO₄, B₂O₃ and carbon as the raw materials. The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated. The results indicated that the onset formation temperature of ZrB₂-SiC was reduced to 1 400 °C by the present conditions, and oxide additive (including CoSO₄·7H₂O, Y₂O₃ and TiO₂) was effective in enhancing the decomposition of raw ZrSiO₄, therefore accelerating the synthesis of ZrB₂-SiC. Moreover, microstructural observation showed that the as-prepared ZrB₂ and SiC respectively had well-defined hexagonal columnar and fibrous morphology. Furthermore, the methodology of back-propagation artificial neural networks (BP-ANNs) was adopted to establish a model for predicting the reaction extent (e g, the content of ZrB₂-SiC in final product) in terms of various processing conditions. The results predicted by the as-established BP-ANNs model matched well with that of testing experiment (with a mean square error in 10^-3 degree), verifying good effectiveness of the proposed strategy.     

熔盐镁热还原法合成ZrB2超细粉体

为了降低ZrB_2粉体的合成温度,并在此基础上合成粒径细小、纯度高的ZrB_2粉体,以ZrO2及B4C为原料,以Mg粉为还原剂,以NaCl-KCl为熔盐介质,研究熔盐镁热还原法低温合成ZrB_2超细粉体的工艺。探讨了反应温度、B/Zr物质的量比及Mg粉用量对合成ZrB_2超细粉体的影响,并对粉体的物相组成及显微结构进行了表征。结果表明合成ZrB_2的起始温度为1 173K,最佳合成温度为1 473K。合成纯相的ZrB_2粉体最佳工艺条件为:B/Zr物质的量比为2.2,Mg过量50%(质量分数),1 473K反应3h。所合成ZrB_2粉体的晶粒尺寸为30~300nm。     

SLM成型件表面球化程度表征方法及等级检测

球化现象是选区激光熔化(SLM)成型过程中最常发生的缺陷,同时影响了最终成型部件的疲劳寿命和物理性能。合理控制部件成型过程中球化现象的发生,对维持成型过程的持续进行以及获得高质量的成型部件具有重大意义。本文在研究SLM成型过程中部件表面球化特征提取方法的基础上提出了球化程度表征方法,并通过正交实验验证了球化程度表征方法的有效性,建立了球化程度与激光能量密度之间的关联关系。同时对球化程度等级做了界定,最终构建了深度卷积神经网络(CNN)模型自动识别部件表面球化程度等级,以辅助实验及生产人员做出相应的决策。模型识别结果表明,在小的图像分割集上,网络识别精度达到了964,当在所采集的全局显微图像集上,其识别精度达到了100,取得了良好的识别效果。本研究将为SLM成型过程中成型质量的实时控制提供有效实现途径。