This study proposes a suitable composite material for acetabular cup replacements in hip joint that involves ultrahigh molecular weight polyethylene, a clinically proven material, as the matrix. To design new ultrahigh molecular weight polyethylene composites with multiple reinforcements for the improvement in mechanical and tribological performance, artificial neural network and genetic algorithm, the two artificial intelligence techniques, are employed. Published reports on the use of ultrahigh molecular weight polyethylene reinforced with multi-walled carbon nanotube and graphene are used as database to develop two artificial neural network models for Young's modulus and tensile strength. The optimum solutions are obtained using genetic algorithm, where the artificial neural network models are used as the objective functions. Two different composites, derived from the optimum solutions, are made reinforcing both multi-walled carbon nanotube and graphene. Tensile and wear tests show significant enhancement in the properties. The structures of the composites are also characterized, and wear mechanisms are discussed.

中文翻译：

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具有多个纳米粒子的超高分子量聚乙烯复合材料的设计：一种人工智能方法

本研究提出了一种适用于髋关节髋臼杯置换的复合材料，其中包括超高分子量聚乙烯，一种临床证明的材料，作为基质。为了设计具有多重增强的新型超高分子量聚乙烯复合材料以提高机械和摩擦学性能，采用人工神经网络和遗传算法这两种人工智能技术。已发表的关于使用多壁碳纳米管和石墨烯增强的超高分子量聚乙烯的报告被用作数据库，以开发杨氏模量和拉伸强度的两个人工神经网络模型。最优解是使用遗传算法获得的，其中人工神经网络模型被用作目标函数。两种不同的复合材料，源自最佳解决方案，可增强多壁碳纳米管和石墨烯。拉伸和磨损测试显示性能显着增强。还表征了复合材料的结构，并讨论了磨损机制。