王松明, 廖映华, 李磊, 廖鑫宇, 李坤. 基于CCD的立式五轴机床滑枕多目标优化[J]. 制造技术与机床, 2023, (11): 154-160. DOI: 10.19287/j.mtmt.1005-2402.2023.11.023
引用本文: 王松明, 廖映华, 李磊, 廖鑫宇, 李坤. 基于CCD的立式五轴机床滑枕多目标优化[J]. 制造技术与机床, 2023, (11): 154-160. DOI: 10.19287/j.mtmt.1005-2402.2023.11.023
WANG Songming, LIAO Yinghua, LI Lei, LIAO Xinyu, LI Kun. Multi-objective optimization of vertical five-axis machine tool ram based on CCD[J]. Manufacturing Technology & Machine Tool, 2023, (11): 154-160. DOI: 10.19287/j.mtmt.1005-2402.2023.11.023
Citation: WANG Songming, LIAO Yinghua, LI Lei, LIAO Xinyu, LI Kun. Multi-objective optimization of vertical five-axis machine tool ram based on CCD[J]. Manufacturing Technology & Machine Tool, 2023, (11): 154-160. DOI: 10.19287/j.mtmt.1005-2402.2023.11.023

基于CCD的立式五轴机床滑枕多目标优化

Multi-objective optimization of vertical five-axis machine tool ram based on CCD

  • 摘要: 滑枕对立式五轴机床的加工精度有显著影响,为了提高滑枕静动态特性,结合滑枕筋板布置,提出了一种基于中心组合设计(central composite design,CCD)的多目标遗传算法优化。在原滑枕结构基础上,设计出5种不同筋板布置结构,在SolidWorks中建立参数化模型,以总变形量、1 阶固有频率和质量作为评价指标。在Workbench中进行静力学与模态分析,对有限元仿真数据综合分析选出井型结构作为优选方案,利用灵敏度分析得出敏感尺寸进行中心组合设计,在Design-Expert中得出试验点的响应值,搭建响应面模型,并对井型滑枕进行多目标优化。优化结果表明:滑枕在总质量增加3.32%的情况下,其最大总变形减小23.25%,一阶固有频率提高10.89%。

     

    Abstract: The ram has a significant influence on the machining accuracy of the vertical five-axis machine tool. In order to improve the static and dynamic characteristics of the ram, a multi-objective genetic algorithm optimization based on the central composite design is proposed in combination with the arrangement of the ram ribs. On the basis of the original ram structure, five different rib layout structures were designed. The parametric model was established in SolidWorks, and the total deformation, first-order natural frequency and mass were used as evaluation indexes. Statics and modal analysis were carried out in Workbench. The well structure was selected as the optimal scheme by comprehensive analysis of finite element simulation data. The sensitive size was obtained by sensitivity analysis and the Central Combination Design was carried out. The response value of the test point was obtained in Design-Expert. The response surface model was built and the multi-objective optimization of the well ram was carried out. The optimization results show that the maximum total deformation of the ram decreases by 23.25% and the first-order natural frequency increases by 10.89% when the total mass increases by 3.32%.

     

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