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Partition weighted sensitivity filtering method based on variable density method
Published:2022-11-22 author:GAO Han, XU Lei, HU Yuan-hao, et al. Browse: 606 Check PDF documents
Partition weighted sensitivity filtering method based on variable density method

GAO Han, XU Lei, HU Yuan-hao, GUO Zhan-ling

(School of Mechanical Engineering, Sichuan University, Chengdu 610065, China)

Abstract:  Aiming at the numerical instability problems including boundary diffusion and slender rod structure of the optimization results in the structural topology optimization based on variable density method, a partitioned weighted sensitivity filtering method based on the variable density method was proposed. Firstly, the sensitivity filtering area was divided into two parts: inner and outer. Then, the values of the optimization parameters such as the weighting factor correction coefficient, the penalty factor, and the minimum filter radius were determined to perform weighting processing on the inner and outer regions respectively by using different weighting factors, so that the regions close to the center obtained higher sensitivity values and the regions far from the center obtained lower sensitivity values, and then a topology optimization structure with clear boundaries was obtained. Finally, after the metrics of topology optimization were introduced, the feasibility and effectiveness of the method in solving boundary diffusion and slender rod problems were verified by typical examples. The research results show that the method improves the number of iterations, gray value, discrete rate and gray rate, which can improve the optimization efficiency and the manufacturability of the optimized structure, and is beneficial to model extraction and reconstruction during postprocessing. Compared with other optimization methods, it has certain advantages in solving boundary diffusion and slender rod problems.

Key words:  variable density method; structure topology optimization; boundary diffusion; slender rod structure; partition weighted sensitivity filtering; weighting factor
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