An AI-Assisted Design Method for Topology Optimization Without Pre-Optimized Training Data
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In this publication, an AI-assisted design method based on topology optimization is presented, which is able to obtain optimized designs in a direct way, without iterative optimum search. The optimized designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling (the volume percentage filled by material) 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 by adapting the predictor's parameters. Other than in state-of-the-art procedures, no pre-optimized geometries are used during training. After the training is completed, the presented AI-assisted design procedure supplies geometries which are similar to the ones generated by conventional topology optimizers, but requires a small fraction of the computational effort required by those algorithms.
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