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.

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Here a method for topology optimization is presented which is able to obtain optimized geometries without iterative optimum search. The optimized geometries are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling as input data. In the training phase, geometries generated on the basis of random input data are evaluated with respect to given criteria and the results of those evaluations flow into an objective function which is minimized. Other than in state-of-the-art procedures, no pre-optimized geometries are used during training.The trained predictor supplies geometries which are similar to the ones generated by conventional topology optimizers, but requires only a small fraction of the computational effort.

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. 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.