ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation

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[Submitted on 7 Jul 2026]

Title:ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation

View a PDF of the paper titled ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation, by Yunhan Xu and 12 other authors

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Abstract:Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution. Existing text-to-CAD methods have made promising progress in generating CAD programs from natural-language descriptions, but they still struggle when user prompts are ambiguous, underspecified, or only describe high-level design intent. They also rarely exploit expert procedural knowledge naturally available in industrial workflows, such as CATIA operation recordings, macro logs, drawing notes, and engineering descriptions. We present \algname, a skill-guided industrial CAD agent with expert-grounded knowledge distillation. The core of \algname is CAD intermediate representation (CAD-IR), an executable procedural representation that encodes parameters, ordered operations, MCP tool bindings, dependencies, generated entities, and verification rules. CAD-IR plays two key roles: it first serves as the carrier for distilling expert CAD procedures into reusable parameterized skills; then it provides a procedural scaffold that turns vague or intermediate-level prompts into complete executable CAD operations. \algname retrieves expert-derived skills, instantiates and revises CAD-IR, executes the resulting procedure through a dedicated CATIA-MCP backend, and uses multi-view visual feedback for iterative refinement, and finally generates production-ready B-Rep models. On the Text2CAD benchmark, CAD-IR improves generation from intermediate prompts by reducing mean Chamfer Distance from $14.83$ to $9.88$, showing its ability to bridge ambiguous textual intent and executable CAD construction. On four complex automotive components, CAD-IR enables expert CATIA recordings to be distilled into reusable skills, allowing \algname to generate editable CATIA-native B-Rep models for new variant requests.

Subjects:

Artificial Intelligence (cs.AI); Graphics (cs.GR)

Cite as: arXiv:2607.05750 [cs.AI]

(or arXiv:2607.05750v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.05750

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yuanwei Bin [view email] [v1] Tue, 7 Jul 2026 02:11:50 UTC (2,825 KB)

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