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Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms

机译:使用进化算法研究金属填料在颗粒增强的柔性模具材料复合物中的作用

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摘要

To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as metallic filler) are included in flexible mould material. But addition of metallic fillers affects various properties of ST process and the influences may vary according to the types of materials used. Therefore, in order to investigate the role of various metallic fillers in particulate reinforced flexible mould material composites, multi-objective optimizations of maximizing equivalent thermal conductivity and minimizing effective modulus of elasticity of composite mould materials are conducted using evolutionary algorithms (EAs). Here we have adopted two EA-based algorithms namely NSGAII and SPEA2 in order to solve the present problem independently. Comparative study of the results reveals that NSGAII performs better over SPEA2 for investigating the role of metallic fillers in particulate reinforced flexible mould material composites. A recently proposed innovization procedure is also used to unveil salient properties associated with the obtained trade-off solutions. These solutions are analyzed to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mould material. Based on the findings through investigations, the optimal selection of materials is suggested including the cost implication factor.
机译:为了减少软模具(ST)过程中的冷却时间,柔性模具材料中包含高导热填料(例如金属填料)。但是,添加金属填料会影响ST工艺的各种性能,并且影响可能会根据所用材料的类型而有所不同。因此,为了研究各种金属填料在颗粒增强的柔性模具材料复合物中的作用,使用演化算法(EA)进行了最大化等效导热系数和最小化复合模具材料的有效弹性模量的多目标优化。在这里,我们采用了两种基于EA的算法,即NSGAII和SPEA2,以便独立解决当前问题。结果的比较研究表明,NSGAII在研究金属填料在颗粒增强的柔性模具材料复合材料中的作用方面优于SPEA2。最近提出的创新程序也用于揭示与获得的权衡解决方案相关的显着特性。分析了这些解决方案,以研究各种参数对复合模具材料的等效热导率和弹性模量的影响。根据调查结果,建议对材料进行最佳选择,包括成本影响因素。

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