Accelerating sensitivity analysis in structural topology optimization using deep neural network

Abstract

In recent years, Topology Optimization (TO) has been increasingly gaining attention with the development of new constructing techniques. In the optimization process of a density-based TO method, the sensitivities of the design variables are often computed using numerical methods like Finite Element Method (FEM). Such operation is performed repetitively for tens or hundreds of iteration steps, therefore generating huge computational cost for large scale design scenarios. This paper proposes to accelerate TO by replacing the full-scale sensitivity analysis of FEM with a reduced-scale case, and adopting a deep neural network to map the reduced-scale sensitivity field back to fine scale. Three neural network models are trained and tested using training data generated by structures of three difference scales. The results show that the proposed network successfully reduced the time cost to a large extent, while preserving the topology of the optimized design.

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