An effective structural boundary processing method based on support vector machine for discrete topology optimization

• Published in: 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) pp. 10 - 15
• Main Authors: Sheng Chu, Mi Xiao, Liang Gao, Lin Gui, Hao Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2016
• Subjects: Data mining; discrete level-set; discrete topology optimization; Mathematical model; Optimization methods; solid isotropic material with penalization (SIMP); Solids; support vector machine (SVM); Support vector machines; Topology
• DOI: 10.1109/CSCWD.2016.7565954

The discrete topology optimization method is widely used because of its high degree of freedom and high problem-solving efficiency. However, one of its main drawbacks is that the output graphic is constituted of a 0-1 matrix leading to rough boundaries and implicit structures, which are difficult to be manufactured. Aiming to transform the implicit rough boundary into the explicit smooth boundary in discrete topology optimization, an effective structural boundary processing method based on support vector machine (SVM) is proposed in this paper. In this method, SVM is used for processing the boundaries obtained by the Solid Isotropic Material with Penalization (SIMP) and discrete level-set methods, respectively. With the SVM method, the clear structural boundaries are obtained and can be used in CAD directly. In order to make structural boundaries more smooth, data filtering on boundaries is employed. The effectiveness of the proposed method is illustrated by the experimental examples. Results show that the proposed method can obtain clear and smooth structural boundaries in discrete topology optimization.