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Erschienen in: Structural and Multidisciplinary Optimization 6/2018

28.11.2017 | RESEARCH PAPER

Optimal topology design of internal stiffeners for machine pedestal structures using biological branching phenomena

verfasst von: Heng Zhang, Xiaohong Ding, Xiaohu Dong, Min Xiong

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 6/2018

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Abstract

Naturally evolved biological structures exhibit the optimal characteristics of light weight, high stiffness, and high strength. Based on the growth mechanism of biological branch systems in nature, an optimization method for internal stiffener plate distribution in box structures is suggested. Under the given load and support conditions, the internal stiffener plates of machine pedestal structures grow, bifurcate, and degenerate towards the direction of maximum overall structural stiffness in accordance with the adaptive growth law. The optimal and distinct distribution of internal stiffener plates with the most effective load path is thus obtained. Based on this, a size optimization for lightweight design is conducted, in which the self-weight of the structures is taken as the design objective, and the natural vibration frequency and static stiffness in the direction that is sensitive to machining accuracy are set as constraints. Finally, an optimized structure is obtained. The effectiveness of the proposed method is verified by using a precision grinder bed as an example. The results of numerical simulation and 3D–printed model experiment indicate that both the dynamic and the static performance of the optimized structure are improved, while the structural weight is reduced by compared with the initial structure. The suggested design method provides a new solution approach for the design optimization of machine pedestal structures.

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Metadaten
Titel
Optimal topology design of internal stiffeners for machine pedestal structures using biological branching phenomena
verfasst von
Heng Zhang
Xiaohong Ding
Xiaohu Dong
Min Xiong
Publikationsdatum
28.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 6/2018
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-017-1862-6

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