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Optimization to improve the Physical and Mechanical Properties of the Electric Power Transmission Wires made from waste using a Genetic Algorithm

机译:优化,以改善遗传算法用垃圾制成的电力传输线的物理和力学性能

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The current study is one of the recent studies prospects in Iraq, where the exploitation of waste empty soft drink cans and scrap damaged electric wires for the manufacture of electric power transmission wires for voltage (11,33,66,132 kv). Practically been improved physical and mechanical properties of the alloy from through thermo-mechanical treatment where the percentage of improvement (electrical conductivity - thermal conductivity - Tensile strength - yield strength - hardness) of the alloy (C8) compared with the alloy base (A) (58%,54%,158.7%,226%,70.7%) respectively. This paper provides method to reach to the optimum alloy using the hybrid method, which is represented by the statistical parameters and genetic algorithms, where the use of statistical data obtained from practical results to determine the optimum properties of alloys (ie, in this research have been identified six of the properties of alloys), accordingly, the database was built describe alloys depending on their properties, then, the evolution algorithm of type breeder genetic algorithm to procedure genetic clustering process and provides a number of required clusters, to avoid the overlapping between clusters with other, one of the clustering validity measures called "Davies-Bouldin index" as fitness function of that algorithm was used. Then was extracted two types of properties for each cluster namely mechanical properties (Tensile strength - yield strength – hardness-elongation) and physical properties (electrical conductivity - thermal conductivity). The proposed methodology achieved 95% accuracy when compare process results with the results of the optimization algorithm.
机译:目前的研究是伊拉克最近的研究前景之一,其中废弃空软饮料罐和废损损坏的电线用于制造电力传输线(11,33,66,132 kV)。通过热机械处理实际改善了合金的物理和力学性能,与合金基座(A)相比,合金(C8)的改善(电导率导热性 - 抗拉强度 - 屈服强度 - 硬度)的百分比(A) (分别为58%,54%,158.7%,226%,70.7%)。本文提供了利用统计参数和遗传算法所代表的混合方法达到最佳合金的方法,其中利用从实际结果获得的统计数据来确定合金的最佳特性(即,在本研究中被确定了六种合金的性质),因此,根据其性质,构建了数据库,随后,繁殖者遗传算法的演化算法到程序遗传聚类过程并提供了许多所需的簇,以避免重叠在与其他算法的群集之间,使用称为“Davies-Bouldin指数”的聚类有效性测量之一作为该算法的健身功能。然后对每个聚类提取两种类型的性质即,即机械性能(拉伸强度 - 屈服强度 - 硬度 - 伸长率和物理性质(导电性 - 导热率)。当使用优化算法的结果进行比较过程结果时,所提出的方法实现了95%的准确性。

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