  Topology optimization is widely used by engineers during the initial product
development process to get a first possible geometry design. The
state-of-the-art is the iterative calculation, which requires both time and
computational power. Some newly developed methods use artificial intelligence
to accelerate the topology optimization. These require conventionally
pre-optimized data and therefore are dependent on the quality and number of
available data. This paper proposes an AI-assisted design method for topology
optimization, which does not require pre-optimized data. The 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. The
results of those evaluations flow into an objective function which is minimized
by adapting the predictor's parameters. 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. We
anticipate our paper to be a starting point for AI-based methods that requires
data, that is hard to compute or not available.
