Initial Design of Trusses using Topology Optimization in a Deep Learning Environment

摘要

Topology optimization is the tool of choice for obtaining the initial design of structural components. The resulting optimal design from topology optimization will be the input for subsequent structural optimizations with regard to shape, size, and layout. In reality, however, iterative solvers used in conventional SIMP (Simplified Isotropic Material with Penalization) based topology optimization schemes consume a very high computational power and therefore act as a bottleneck in the manufacturing process. In this work, an accelerated topology optimization technique based on deep learning is presented. Conditional Generative Adversarial Network (cGAN) architecture is used to predict the optimal topology of a given structure subject to a set of input parameters. This novel framework is showcased to generate initial truss designs for any combination of volume fractions and edge loading locations. Unlike prevalent topology optimization solvers, the proposed method obtains the accurate topologically optimised truss structure within milliseconds.