  We propose a neural network-based approach to topology optimization that aims
to reduce the use of support structures in additive manufacturing. Our approach
uses a network architecture that allows the simultaneous determination of an
optimized: (1) part segmentation, (2) the topology of each part, and (3) the
build direction of each part that collectively minimize the amount of support
structure. Through training, the network learns a material density and segment
classification in the continuous 3D space. Given a problem domain with
prescribed load and displacement boundary conditions, the neural network takes
as input 3D coordinates of the voxelized domain as training samples and outputs
a continuous density field. Since the neural network for topology optimization
learns the density distribution field, analytical solutions to the density
gradient can be obtained from the input-output relationship of the neural
network. We demonstrate our approach on several compliance minimization
problems with volume fraction constraints, where support volume minimization is
added as an additional criterion to the objective function. We show that
simultaneous optimization of part segmentation along with the topology and
print angle optimization further reduces the support structure, compared to a
combined print angle and topology optimization without segmentation.
