  Generative design (GD) methods aim to automatically generate a wide variety
of designs that satisfy functional or aesthetic design requirements. However,
research to date generally lacks considerations of manufacturability of the
generated designs. To this end, we propose a novel GD approach by using deep
neural networks to encode design for manufacturing (DFM) rules, thereby
modifying part designs to make them manufacturable by a given manufacturing
process. Specifically, a three-step approach is proposed: first, an instance
segmentation method, Mask R-CNN, is used to decompose a part design into
subregions. Second, a conditional generative adversarial neural network (cGAN),
Pix2Pix, transforms unmanufacturable decomposed subregions into manufacturable
subregions. The transformed subregions of designs are subsequently reintegrated
into a unified manufacturable design. These three steps, Mask-RCNN, Pix2Pix,
and reintegration, form the basis of the proposed Manufacturable conditional
GAN (McGAN) framework. Experimental results show that McGAN can transform
existing unmanufacturable designs to generate their corresponding
manufacturable counterparts automatically that realize the specified
manufacturing rules in an efficient and robust manner. The effectiveness of
McGAN is demonstrated through two-dimensional design case studies of an
injection molding process.
