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首页> 外文期刊>Concurrent engineering >Multi-objective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance
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Multi-objective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance

机译:龙门机床中龙门的多目标选择和结构优化,以提高静态,动态以及重量和成本性能

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摘要

In this investigation, the multi-objective selection and optimization of a gantry machine tool is achieved by analytic hierarchy process, multi-objective genetic algorithm, and Pareto-Edgeworth-Grierson-multi-criteria decision-making method. The objectives include maximum static deformation, the first four natural frequencies, mass, and fabrication cost of the gantry. Further structural optimization of the best configuration was accomplished using multi-objective genetic algorithm to improve all objectives except cost. The result of sensitivity analysis reveals the major contribution of columns of gantry with respect to the crossbeam's contribution. After determining the most effective geometrical parameters using sensitivity analysis, multi-objective genetic algorithm was performed to obtain the Pareto-optimal solutions. In order to choose the final configuration, Pareto-Edgeworth-Grierson-multi-criteria decision-making was applied. The procedure outlined in this article could be used for selection and optimization of gantry as quantitative method as opposed to traditional qualitative method exploited in industrial application for design of gantry.
机译:在这项研究中,龙门机床的多目标选择和优化是通过层次分析法,多目标遗传算法和Pareto-Edgeworth-Grierson多准则决策方法实现的。目标包括最大的静态变形,前四个固有频率,质量以及龙门架的制造成本。使用多目标遗传算法完成了最佳配置的进一步结构优化,以改善除成本以外的所有目标。灵敏度分析的结果表明,相对于横梁的贡献,龙门柱的主要贡献。在使用敏感性分析确定最有效的几何参数之后,执行多目标遗传算法以获得帕累托最优解。为了选择最终配置,应用了Pareto-Edgeworth-Grierson-多标准决策。本文概述的程序可用于龙门的选择和优化,作为定量方法,与工业设计中用于龙门设计的传统定性方法相反。

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