McGAN: Generating Manufacturable Designs by Embedding Manufacturing Rules into Conditional Generative Adversarial Network (2407.16943v1)

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.

• The paper introduces McGAN, a framework that embeds DFM rules into a cGAN to automatically convert unmanufacturable designs.
• It uses Mask R-CNN for instance segmentation and Pix2Pix to transform subregions, ensuring compliance with injection molding standards.
• Experimental results demonstrate superior performance against alternative methods, achieving high precision and improved design similarity.

Overview of McGAN: A Framework for Manufacturable Generative Design

The paper "McGAN: Generating Manufacturable Designs by Embedding Manufacturing Rules into Conditional Generative Adversarial Network" contributes to the field of generative design (GD) by introducing an innovative approach that integrates design for manufacturing (DFM) rules into the generative process. This work aims to address a significant gap in GD research, which often overlooks manufacturability despite its importance in practical applications. The proposed framework, McGAN, is structured to automatically transform unmanufacturable designs into manufacturable ones, thereby embedding manufacturing feasibility into the generative design process.

McGAN Framework

The McGAN framework is structured around three primary stages: instance segmentation using Mask R-CNN, transformation of unmanufacturable subregions via a conditional generative adversarial network (cGAN) known as Pix2Pix, and reintegration of transformed subregions into a unified manufacturable design.

1. Instance Segmentation with Mask R-CNN: The use of Mask R-CNN in McGAN facilitates the decomposition of complex part designs into distinct subregions. This step is critical in identifying features that are unmanufacturable, thereby allowing for targeted modifications.
2. Transformation with Pix2Pix: Following segmentation, the cGAN Pix2Pix is employed to convert these unmanufacturable subregions into manufacturable ones. This transformation encodes specific DFM rules related to the manufacturing process, such as those associated with injection molding, into the generative process.
3. Reintegration: The transformed subregions are then reintegrated to form a final design that satisfies both the functional requirements and manufacturability constraints.

Numerical Results and Validation

The paper presents robust experimental results, primarily focusing on two-dimensional design case studies associated with the injection molding process. McGAN successfully demonstrates its capability to modify designs by applying DFM rules such as adding draft angles, adjusting aspect ratios, and rounding sharp corners. The effectiveness is quantitatively measured using evaluation metrics like average precision (AP) and Frechet Inception Distance (FID), reflecting high precision in instance segmentation and substantial improvements in design similarity post-modification.

Comparison with Other Methods

The paper further evaluates McGAN against alternative methods such as PSPNet and DDPM. These comparisons indicate superior performance of McGAN, particularly with respect to design modification quality and computational efficiency. The paper demonstrates that Pix2Pix is effective for manufacturability modification tasks, outperforming other segmentation and generative techniques.

Implications and Future Directions

The implications of this work are substantial, providing a scalable method for integrating manufacturability considerations into automated design processes—a crucial step for concurrent engineering practices. The generalizability of McGAN to accommodate different manufacturing features and processes, although initially focused on injection molding, holds considerable promise for broader applications.

The paper identifies several future research directions, including:

• Addressing the problem of accommodating designs with varying scales.
• Extending the framework to cover a wider range of manufacturing processes and feature complexities.
• Investigating the integration of design for functionality alongside manufacturability, enabling a balanced generative process that satisfies both criteria.

Overall, the McGAN framework represents a thoughtful advancement in the field of generative design, embedding crucial manufacturability constraints into the generative neural networks to streamline and optimize design-to-manufacturing pipelines. The future exploration of these identified research areas and potential extensions promises to further enrich the capabilities and applicability of GD methods in the manufacturing spectrum.