McGAN: Generating Manufacturable Designs by Embedding Manufacturing Rules into Conditional Generative Adversarial Network

TL;DR

This paper introduces McGAN, a deep learning framework that automatically transforms unmanufacturable designs into manufacturable ones by embedding manufacturing rules into a conditional GAN, demonstrated on injection molding cases.

Contribution

The paper presents a novel three-step deep neural network approach combining Mask R-CNN and Pix2Pix to encode manufacturing rules into design generation, enabling automatic manufacturability.

Findings

Successfully transforms unmanufacturable designs into manufacturable designs.

Efficient and robust process demonstrated on 2D injection molding cases.

Automates manufacturability compliance in generative design workflows.

Abstract

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.