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基于Ansys的新型双层蜂巢板抗压性能研究及优化设计 被引量:2

Compressive Performance and Optimal Design of New Double-Layer Honeycomb Panel Based on Ansys
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摘要 目的利用Ansys研究一种新型双层蜂巢板的结构特征来优化该蜂巢板的结构参数,提高其抗压性能。方法对蜂巢板进行压力试验和模型验证,利用响应面结果分析蜂巢单元的单元高度、厚度,以及中间面板厚度与蜂巢板平压最大等效应力的关系,最后利用响应面优化对蜂巢板进行多目标优化。结果蜂巢板的平压最大等效应力与蜂巢单元高度、壁厚、中间面板厚度都有很大的关系,优化后的蜂巢的质量由8.9979 g降至7.8215 g,下降了13.07%,而蜂巢板的最大应力基本无变化。结论通过Ansys Workbench的多目标优化,在保证蜂巢板抗压性能的情况下,有效地降低了蜂巢板的质量,同时也证明了此类方案的有效性。 To use Ansys to study the structural characteristics of a new type of double-layer honeycomb panel to optimize the structural parameters of the honeycomb panel and improve its compressive performance.Pressure test and model verification were performed on the honeycomb panel.The response surface results were used to analyze the cell height and thickness of the honeycomb element,as well as the relationship between the thickness of the middle panel and the stiffness of the honeycomb panel.Finally,the response surface optimization was used to optimize the honeycomb panel with multiple objectives.The stiffness of the honeycomb panel has a great relationship with the height of the honeycomb element,the wall thickness,and the thickness of the middle panel.The quality of the optimized honeycomb is reduced from 8.9979 g to 7.8215 g,a decrease of 13.07%,while the stiffness of the honeycomb panel is basically unchanged.Through the multi-objective optimization of Ansys Workbench,while ensuring the rigidity of the honeycomb plate,the quality of the honeycomb plate is effectively reduced,and the effectiveness of this type of solution is also proved.
作者 廖雁兵 宋海燕 王立军 边继庆 LIAO Yan-bing;SONG Hai-yan;WANG Li-jun;BIAN Ji-qing(Tianjin University of Science and Technology,Tianjin 300222,China;Key Laboratory of Food Packaging Materials and Technology of China Light Industry,Tianjin 300222,China;Anqiu Tianliyuan New Material Co.,Ltd.,Anqiu 262100,Shandong,China)
出处 《包装工程》 CAS 北大核心 2021年第7期113-119,共7页 Packaging Engineering
基金 天津市自然科学基金(17JCTPJC55800)。
关键词 塑料蜂巢板 ANSYS 优化设计 多目标优化 plastic honeycomb board Ansys optimized design multi-objective optimization
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