Graded multiscale topology optimization using neural networks

被引：18
|
作者：
Chandrasekhar, Aaditya [1 ]
Sridhara, Saketh [1 ]
Suresh, Krishnan [1 ]
机构：
[1] Univ Wisconsin, Dept Mech Engn, Madison, WI 53706 USA
来源：
ADVANCES IN ENGINEERING SOFTWARE | 2023年 / 175卷
基金：
美国国家科学基金会;
关键词：
Multiscale topology optimization; Graded microstructure; Neural networks; Automatic differentiation; LEVEL-SET; NONUNIFORM MICROSTRUCTURES; CELLULAR STRUCTURES; DESIGN; HOMOGENIZATION;
D O I：
10.1016/j.advengsoft.2022.103359
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, we propose a novel graded multiscale topology optimization framework by exploiting the unique classification capacity of neural networks. The salient features of this framework include: (1) the number of design variables is only weakly dependent on the number of pre-selected microstructures, (2) it guarantees partition of unity while discouraging microstructure mixing, (3) it supports automatic differentiation, thereby eliminating manual sensitivity analysis, and (4) it supports high-resolution re-sampling, leading to smoother variation of microstructure topologies. The proposed framework is illustrated through several examples.
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页数：10
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