作　　者： ; ; ; ;

机构地区： 东北大学材料与冶金学院

出　　处： 《功能材料》 2006年第6期952-954,958,共4页

摘　　要： 基于BP神经网络的原理,建立了热爆法合成多孔NiTi形状记忆合金孔隙度和压缩屈服强度的神经网络模型。该模型拟合了温度、Ti粉颗粒尺寸和生坯密度对热爆产物孔隙度和压缩屈服强度的影响。当温度、Ti粉颗粒尺寸和生坯密度发生变化时,对反应产物的性能进行了预测,其预测值和实验值非常吻合。因此该模型可用于多孔NiTi形状记忆合金的性能控制,通过对性能的合理调控,使其与所替换的生物组织更加匹配,同时提高研究效率,降低成本。 Based on BP neutral network theory,the model has been developed for the prediction of porosity and compressive yield strength of porous NiTi shape memory alloy（SMA） prepared by thermal explosion. The results indicate that the model is capable of reproducing the effects of changes in temperature,particle size of Ti and green density on product porosity and compressive yield strength. When temperature,particle size of Ti and green porosity changed, the product properties are predicted. Good agreement between predicted and experimental data is obtained. So the model can be used in the quality control of porous NiTi SMA. By the proper adjustment of the properties,porous NiTi SMA can better match the replaced tissue. This way can improve the efficiency and reduce the cost.

关 键 词： 神经网络 热爆 形状记忆合金 孔隙度 屈服强度

领　　域： [一般工业技术]