遗传算法在复合材料成分优化中的应用

Application of genetic algorithm to optimization of composition admeasurements for compound material

金属塑料复合材料有广阔的应用前景，对其研究的一个重要方面是其工作层成分的配比．提出综合运用自适应神经模糊推理及遗传算法，以复合材料结合牢固性及减振性好为目标，最终得出工作层成分最佳配比的优化方法．通过试验得出，当工作层成分体积分数为：聚己二酸己二胺（PA66／尼龙66）51．6％，聚苯硫醚（PPS）38．6％，碳纤维9．8％，所制备的金属塑料复合材料与用普通方法制备的金属塑料复合材料相比，减振性能提高9％～15％，材料在不发生脱层的前提下所能承受的最大冲击力提高8％～16％，复合材料的综合性能有较大提高．

Metal-plastic compound material has a wide application prospect and composition ad- measurements of working layer are one of the key aspects of research on it. An optimal method for composition admeasurements of working layer based on adaptive neuro-fuzzy inference and genetic algorithm by taking firmness and vibration reduction as the objective was presented. The results show that the optimal volume ratios of working layer were.. PA66 51.6%, PPS 38.6%, carbon fiber 9.8 %. Comparing to the metal-plastic compound material made by common method, the one with the optimal composition admeasurements achieves a 9%-15% vibration reduction and a 8%-16% increase in impact force bearing on condition that no failure happens. The comprehensive properties of metal-plastic compound materials are greatly improved.