学术文献库

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.