ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation
Summary
ArtisanCAD is a new skill-guided industrial computer-aided design (CAD) agent that employs expert-grounded knowledge distillation to address limitations in existing text-to-CAD methods. Current approaches struggle with ambiguous or underspecified user prompts and often fail to leverage expert procedural knowledge from industrial workflows like CATIA operation recordings. ArtisanCAD's core is its CAD Intermediate Representation (CAD-IR), an executable procedural format encoding parameters, operations, and dependencies. CAD-IR distills expert CAD procedures into reusable parameterized skills and scaffolds vague prompts into complete operations. The agent retrieves these skills, instantiates and revises CAD-IR, executes via a CATIA-MCP backend with multi-view visual feedback, and generates production-ready B-Rep models. On the Text2CAD benchmark, CAD-IR reduced mean Chamfer Distance from 14.83 to 9.88 for intermediate prompts, demonstrating its ability to bridge textual intent and executable CAD construction. It also distills expert CATIA recordings for complex automotive components, enabling generation of editable CATIA-native B-Rep models for new variants.
Key takeaway
For AI Engineers developing industrial CAD automation, you should explore incorporating expert-grounded knowledge distillation and intermediate representations like CAD-IR. This approach significantly improves the robustness of text-to-CAD systems, enabling them to handle ambiguous prompts and generate production-grade B-Rep models. Consider distilling existing CATIA operation recordings into reusable skills to accelerate the creation of editable CAD models for new design variants.
Key insights
ArtisanCAD distills expert CAD procedures into reusable skills via CAD-IR to generate production-ready B-Rep models from ambiguous prompts.
Principles
- Expert procedural knowledge is a valuable, underutilized resource in CAD.
- Intermediate representations can bridge high-level intent and low-level execution.
- Iterative refinement with visual feedback improves CAD generation.
Method
ArtisanCAD retrieves expert-derived skills, instantiates and revises CAD-IR, executes via a CATIA-MCP backend, and refines using multi-view visual feedback to generate B-Rep models.
In practice
- Distill CATIA operation recordings into parameterized skills.
- Use CAD-IR to convert vague design intent into executable CAD operations.
- Apply multi-view visual feedback for iterative model refinement.
Topics
- ArtisanCAD
- Text-to-CAD
- Knowledge Distillation
- CAD Intermediate Representation
- B-Rep Modeling
- CATIA Automation
Best for: Research Scientist, AI Scientist, AI Engineer, Machine Learning Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.