  Mechanical product engineering often must comply with manufacturing or
geometric constraints related to the shaping process. Mechanical design hence
should rely on robust and fast tools to explore complex shapes, typically for
design for additive manufacturing (DfAM). Topology optimization is such a
powerful tool, yet integrating geometric constraints (shape-related) into it is
hard. In this work, we leverage machine learning capability to handle complex
geometric and spatial correlations to integrate into the mechanical design
process geometry-related constraints at the conceptual level. More precisely,
we explore the generative capabilities of recent Deep Learning architectures to
enhance mechanical designs, typically for additive manufacturing. In this work,
we build a generative Deep-Learning-based approach of topology optimization
integrating mechanical conditions in addition to one typical manufacturing
condition (the complexity of a design i.e. a geometrical condition). The
approach is a dual-discriminator GAN: a generator that takes as input the
mechanical and geometrical conditions and outputs a 2D structure and two
discriminators, one to ensure that the generated structure follows the
mechanical constraints and the other to assess the geometrical constraint. We
also explore the generation of designs with a non-uniform material distribution
and show promising results. Finally, We evaluate the generated designs with an
objective evaluation of all wanted aspects: the mechanical as well as the
geometrical constraints.
