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针对传统聚合物黏度模型参数拟合方法中存在的初值选取问题及权重分配问题,采用基于Pareto多目标遗传算法的拟合方法。根据实验测得PP和PMMA材料流变数据对常用的Cross-WLF 7参量黏度模型进行了拟合,拟合结果的复相关系数分别达到了0.999929和0.999036。研究结果为聚合物注射成型仿真提供了必需的材料流变数据,为塑料制品的模具设计、工艺参数优化和质量预测提供了理论依据。
Aiming at the problem of initial value selection and weight distribution in traditional polymer viscosity model parameter fitting method, a fitting method based on Pareto multi-objective genetic algorithm is adopted. According to the experimental rheological data of PP and PMMA materials, the commonly used Cross-WLF 7 viscosity model was fitted. The complex correlation coefficients of fitting results reached 0.999929 and 0.999036, respectively. The results provide the necessary material rheological data for the polymer injection molding simulation, which provides a theoretical basis for the plastic mold design, process parameter optimization and quality prediction.