( 1. 中南大学 粉末冶金国家重点实验室，长沙 410083;
2. 中南大学 机电工程学院， 长沙 410083)
摘 要: 针对喂料粘度模型参数求解和现有模流分析软件无拟合功能的问题， 引入Cross-WLF七参数模型对MIM中非牛顿流体流动过程进行了研究， 提出了自适应快速遗传算法拟合该模型参数， 开发了粘度模型参数拟合求解器， 得到了W-Ni-Fe高密度粉末喂料和316L 不锈钢喂料粘度模型的7 个参数， 拟合结果的复合相关系数分别达到0.998 489和0.998 200。 研究结果为高密度类零件和不锈钢类的质量预测、 模具和工艺参数优化设计提供了必须的材料数据。
关键字: 金属粉末注射成形； 遗传算法； 参数拟合； 粘度模型
（ 1. State Key Laboratory of Powder Metallurgy,
Central South University, Changsha 410083, China;
2. School of Mechanical and Electrical Engineering,
Central South University, Changsha 410083, China）
Abstract: For solution of feedstock viscosity model parameters and shortage of regress function in current molding analysis software, the flow process of Non-Newtonian feedstock fluid was firstly studied by using Cross-WLF Seven-Parameter model and a Quick Self-adaptive Genetic Algorithm was built and the viscosity model parameter fitting solver was realized. The seven parameters of Cross viscosity model of W-Ni-Fe and 316L stainless MIM feedstock were fitted, the multicorrelation coefficients (R) of fitting results are 0.998 489 and 0.998 200, which means its accuracy is very high. Then the necessary material data was obtained for quality forecast of mold core part made of stainless steel and heavy alloy tungsten ball, and for optimal design of mold and process parameters by this way. It establishes the foundation of material database of MIM.
Key words: metal powder injection molding; genetic algorithm; parameters fit; viscosity model
