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Learning From Design Procedure To Generate CAD Programs for Data Augmentation

Yan-Ying Chen, Dule Shu, Matthew Hong, Andrew Taber, Jonathan Li, Matthew Klenk

TL;DR

A novel data augmentation paradigm that prompts an LLM to generate CAD programs conditioned on a reference surface program and a modeling procedure is proposed - an idea inspired by practices in industrial design that enriches the geometric distribution of generated CAD models.

Abstract

Large Language Models (LLMs) have demonstrated impressive capabilities in a wide range of code generation tasks. However, generating code for certain domains remains challenging. One such domain is Computer-Aided Design (CAD) program, where the goal is to produce scripted parametric models that define object geometry for precise design and manufacturing applications. A key challenge in LLM-based CAD program generation is the limited geometric complexity of generated shapes compared to those found in real-world industrial designs. This shortfall is in part due to the lack of diversity in the available CAD program training data. To address this, we propose a novel data augmentation paradigm that prompts an LLM to generate CAD programs conditioned on a reference surface program and a modeling procedure - an idea inspired by practices in industrial design. By varying the reference surface using a collection of organic shapes, our method enriches the geometric distribution of generated CAD models. In particular, it introduces edges and faces defined by spline-based curvature, which are typically missing or underrepresented in existing open-source CAD program datasets. Experiments show that our method produces CAD samples with significantly greater geometric diversity and a higher resemblance to industry-grade CAD designs in terms of the proportion of organic shape primitives. This enhancement makes our CAD data augmentation approach a useful tool for training LLMs and other deep learning models in CAD generation.

Learning From Design Procedure To Generate CAD Programs for Data Augmentation

TL;DR

A novel data augmentation paradigm that prompts an LLM to generate CAD programs conditioned on a reference surface program and a modeling procedure is proposed - an idea inspired by practices in industrial design that enriches the geometric distribution of generated CAD models.

Abstract

Large Language Models (LLMs) have demonstrated impressive capabilities in a wide range of code generation tasks. However, generating code for certain domains remains challenging. One such domain is Computer-Aided Design (CAD) program, where the goal is to produce scripted parametric models that define object geometry for precise design and manufacturing applications. A key challenge in LLM-based CAD program generation is the limited geometric complexity of generated shapes compared to those found in real-world industrial designs. This shortfall is in part due to the lack of diversity in the available CAD program training data. To address this, we propose a novel data augmentation paradigm that prompts an LLM to generate CAD programs conditioned on a reference surface program and a modeling procedure - an idea inspired by practices in industrial design. By varying the reference surface using a collection of organic shapes, our method enriches the geometric distribution of generated CAD models. In particular, it introduces edges and faces defined by spline-based curvature, which are typically missing or underrepresented in existing open-source CAD program datasets. Experiments show that our method produces CAD samples with significantly greater geometric diversity and a higher resemblance to industry-grade CAD designs in terms of the proportion of organic shape primitives. This enhancement makes our CAD data augmentation approach a useful tool for training LLMs and other deep learning models in CAD generation.
Paper Structure (17 sections, 1 equation, 6 figures, 3 tables)

This paper contains 17 sections, 1 equation, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Method
  4. Design Procedure Prompting
  5. Reference Surface Program
  6. Program Generation and Validation
  7. Experiments
  8. Experiment Setting
  9. Geometric Properties of Programmed CAD Data
  10. Diversity of Shape Complexity
  11. Ablation Study on Design Procedure Prompting
  12. Program Modularization for Design Procedure
  13. Generalizability to other Design Targets
  14. Limitation and Discussion
  15. Conclusion
  16. ...and 2 more sections

Figures (6)

  • Figure 1: It is common to use a reference surface to guide CAD creation for specific design intent, e.g., compatibility to other component. Motivated by this idea, we propose to a new design procedure prompting to guide the CAD program generation toward more organic shapes.
  • Figure 2: System overview: (1) Design procedure prompting takes a design description and a reference surface program as an input to formulate the design procedure. (2) The design prompt then condition an LLM to generate a CAD program. (3) Program validation executes the generated program to visualize a CAD Brep. (4) Structure validation checks the validity of the CAD Brep.
  • Figure 3: Distribution of B-Spline ratio over data samples: Industry, DeepCAD-b and ours only contain bracket data. ABC, GenCAD and CAD-MLLM include brackets and other objects.
  • Figure 4: Examples of CAD B-rep visualization of our approach using reference surfaces, and the alternatives (-RT) and (-R) excluding reference surfaces. (-R) include a text guidance to prompt smooth and organic shapes. Ours with reference surfaces can generate more B-Spline shapes.
  • Figure 5: Visualization of the generated program modules. The left side of each column is a generated bracket with its reference surface, and the right side is the bracket after the reference surface is removed.
  • ...and 1 more figures