中文摘要：

为进一步提高聚合物复合材料热导率,采用多尺度数值预测法研究了微注塑聚酰胺/碳纤维（PA66/CFs）散热器内部CF的流动诱导取向及其对制品热导率的影响规律。首先,利用Moldflow获取CF取向张量,并以Comsol Multiphysics构建与之对应的复合材料微元胞。利用正交实验法研究熔体温度、模具温度、最大注射压力及注射流率对微散热器热导率的影响。然后,对预测数据进行分析获得最优注塑参数组合。最后,对优化结果进行模拟实验,验证了多尺度数值预测法的有效性。结果显示：上述各参数重要程度由大到小依次排列为熔体温度、注射流率、最大注射压力和模具温度;最佳组合为熔体温度360℃、模具温度70℃、最大注射压力220 MPa及注射流率3×10–4 cm3/s。另外,流动诱导热导率变化最大值达0.36 W/（m·K）,为基体热导率的1.5倍。得到的研究结果为从工艺调控的新角度来改善聚合物复合材料的导热性能提供了理论依据与数据支撑。

英文摘要：

To further increase the thermal conductivity of polymer composites, a multi-scale numerical prediction method was applied to investigation of the CF flow induced orientation in micro-injection molded polyamide/carbon fibers （PA66/CFs） micro heat sink as well as its influence on the thermal conductivity of composites. Firstly, the Moldflow was used to determine the orientation tensor of the CF, the Comsol Multiphysics was used to develop the corresponding micro cell of the composite, and an orthogonal experiment method was utilized to study the effects of processing parameters including melt temperature, mold temperature, maximal injection pressure and injection flow rate, on the thermal conductivity. Then, on the basis of prediction data analysis, the optimal combination of the injection molding processing parameters was obtained. Finally, the optimal combination results were confirmed using simulated experiment, which verified the feasibility of the proposed multi-scale numerical prediction method. The results show that the influential parameters in descending order of importance are melt temperature, injection flow rate, maximal injection pressure, and mold temperature. Moreover, the obtained optimal combination of the investigated factors is identified as the melt temperature of 360 ℃, mold temperature of 70 ℃, maximal injection pressure of 220 MPa and the injection flow rate of 3 -10-4 cm3/s. Inaddition, the maximal variation of the flow-induced thermal conductivity is determined as 0.36 W/（m.K）, which is 1.5 times that of polymer matrix. The findings in the present work provide theoretical basis and data supports for further increasing the thermal conductivity of polymer composites from a new viewpoint of processing control.