An Artificial Intelligence–Assisted Design Method for Topology Optimization without Pre-Optimized Training Data

Abstract

Engineers widely use topology optimization during the initial process of product development to obtain a first possible geometry design. The state-of-the-art method is iterative calculation, which requires both time and computational power. This paper proposes an AI-assisted design method for topology optimization, which does not require any optimized data. An artificial neural network—the predictor—provides the designs on the basis of boundary conditions and degree of filling as input data. In the training phase, the so-called evaluators evaluate the generated geometries on the basis of random input data with respect to given criteria. The results of those evaluations flow into an objective function, which is minimized by adapting the predictor’s parameters. After training, the presented AI-assisted design procedure generates geometries that are similar to those of conventional topology optimizers, but require only a fraction of the computational effort. We believe that our work could be a clue for AI-based methods that require data that are difficult to compute or unavailable.

Deep learning in computational mechanics: a review

“…Another option of introducing NNs to the optimization loop is to use NNs as an ansatz of λ, see, e.g., [313,444,[466][467][468][469][470][471][472][473][474]. In the context of inverse problems [313,444,[466][467][468][469][470], the NN acts as regularizer on a spatially varying inverse quantity λ(x) = I N N (x; θ ), providing both smoother and sharper solutions. For topology optimization with a NN parametrization of the density function [471][472][473][474], no regularizing effect was observed.…”

Topology Optimisation under Uncertainties with Neural Networks

“…Examples are only shown for a coarse mesh discretisation of the design domain. In [16], an NN-assisted design method for topology optimisation is devised, which does not require any optimised data. A predictor NN provides the designs on the basis of boundary conditions and degree of filling as input data for which no optimisation training data are required.…”

Análise de sensibilidade de variação finita assistida por redes neurais artificiais para concepção de metamateriais

“…No aprendizado não supervisionado, o modelo busca detectar padrões nos dados, sem a disponibilização de saídas esperadas. Esse tipo de aprendizado pode ser utilizado para gerar redes que otimizam estruturas sem a necessidade de realizar diversas otimizações prévias para gerar dados de treino (HALLE et al, 2021), ou para pós-processar estruturas otimizadas através de processos de desomogeneização (ELINGAARD et al, 2022).…”