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

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.

Automated summary

This paper introduces an AI-assisted design method for topology optimization based on an artificial neural network, which generates geometries similar to conventional topology optimizers after training the predictor, but with significantly less computational effort.