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Deep Generative Design for Mass Production

Jihoon Kim, Yongmin Kwon, Namwoo Kang

TL;DR

This research introduces an innovative framework addressing manufacturability concerns by integrating constraints pertinent to die casting and injection molding into GD, through the utilization of 2D depth images, and enhances this approach by adopting an advanced 2D generative model, which offer a more efficient alternative to traditional 3D shape generation methods.

Abstract

Generative Design (GD) has evolved as a transformative design approach, employing advanced algorithms and AI to create diverse and innovative solutions beyond traditional constraints. Despite its success, GD faces significant challenges regarding the manufacturability of complex designs, often necessitating extensive manual modifications due to limitations in standard manufacturing processes and the reliance on additive manufacturing, which is not ideal for mass production. Our research introduces an innovative framework addressing these manufacturability concerns by integrating constraints pertinent to die casting and injection molding into GD, through the utilization of 2D depth images. This method simplifies intricate 3D geometries into manufacturable profiles, removing unfeasible features such as non-manufacturable overhangs and allowing for the direct consideration of essential manufacturing aspects like thickness and rib design. Consequently, designs previously unsuitable for mass production are transformed into viable solutions. We further enhance this approach by adopting an advanced 2D generative model, which offer a more efficient alternative to traditional 3D shape generation methods. Our results substantiate the efficacy of this framework, demonstrating the production of innovative, and, importantly, manufacturable designs. This shift towards integrating practical manufacturing considerations into GD represents a pivotal advancement, transitioning from purely inspirational concepts to actionable, production-ready solutions. Our findings underscore usefulness and potential of GD for broader industry adoption, marking a significant step forward in aligning GD with the demands of manufacturing challenges.

Deep Generative Design for Mass Production

TL;DR

This research introduces an innovative framework addressing manufacturability concerns by integrating constraints pertinent to die casting and injection molding into GD, through the utilization of 2D depth images, and enhances this approach by adopting an advanced 2D generative model, which offer a more efficient alternative to traditional 3D shape generation methods.

Abstract

Generative Design (GD) has evolved as a transformative design approach, employing advanced algorithms and AI to create diverse and innovative solutions beyond traditional constraints. Despite its success, GD faces significant challenges regarding the manufacturability of complex designs, often necessitating extensive manual modifications due to limitations in standard manufacturing processes and the reliance on additive manufacturing, which is not ideal for mass production. Our research introduces an innovative framework addressing these manufacturability concerns by integrating constraints pertinent to die casting and injection molding into GD, through the utilization of 2D depth images. This method simplifies intricate 3D geometries into manufacturable profiles, removing unfeasible features such as non-manufacturable overhangs and allowing for the direct consideration of essential manufacturing aspects like thickness and rib design. Consequently, designs previously unsuitable for mass production are transformed into viable solutions. We further enhance this approach by adopting an advanced 2D generative model, which offer a more efficient alternative to traditional 3D shape generation methods. Our results substantiate the efficacy of this framework, demonstrating the production of innovative, and, importantly, manufacturable designs. This shift towards integrating practical manufacturing considerations into GD represents a pivotal advancement, transitioning from purely inspirational concepts to actionable, production-ready solutions. Our findings underscore usefulness and potential of GD for broader industry adoption, marking a significant step forward in aligning GD with the demands of manufacturing challenges.
Paper Structure (15 sections, 2 equations, 8 figures)

This paper contains 15 sections, 2 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Shape Reconstruction
  3. Dataset
  4. Depth Calculation
  5. Manufacturable Shape Reconstruction
  6. Design Generation
  7. Denoising Diffusion Probabilistic Models
  8. Forward Process
  9. Reverse Process
  10. Adaptation to Depth Images
  11. Edge Detection
  12. Training and Generation
  13. Conclusion
  14. Summary
  15. Limitations and Future Work

Figures (8)

  • Figure 1: The framework of the proposed method, highlighting its capability to: (a) transform 3D shapes into designs suitable for mass production by using reconstructions from two 2D depth images, (b) while also leveraging powerful 2D generative models to create novel, diverse, and manufacturable 3D designs
  • Figure 2: Calculating the distances between the top and bottom planes allows for the creation of 2D depth grids representing 3D shapes. This process facilitates the reconstruction of shapes, inherently removing any overhangs that cannot be manufactured.
  • Figure 3: SimJEB engine bracket dataset whalen2021simjeb
  • Figure 4: A subset of the calculated depth images
  • Figure 5: Illustration of the original and the reconstructed (recon.) shapes. The top and the bottom are identical.
  • ...and 3 more figures