Aligning Constraint Generation with Design Intent in Parametric CAD

2025-04-17

AI Summary

In parametric CAD, conventional sketch constraint generation often misaligns with design intent, leading to over-constrained systems or geometric distortions. To address this, we propose “Design Alignment”—a novel paradigm that introduces large language model (LLM) alignment techniques to CAD constraint generation for the first time. Our method establishes a solver-feedback-driven alignment training framework, integrating reasoning-capable LLMs’ semantic understanding with classical geometric constraint modeling to jointly optimize constraint completeness and geometric fidelity. The approach is compatible with existing generative models and achieves a full-constraint satisfaction rate of 93%, substantially outperforming supervised fine-tuning baselines (34%) and non-aligned methods (8.9%). This advancement significantly enhances the editability, robustness, and design-intent consistency of parametric CAD models.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Satisfaction
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

We adapt alignment techniques from reasoning LLMs to the task of generating engineering sketch constraints found in computer-aided design (CAD) models. Engineering sketches consist of geometric primitives (e.g. points, lines) connected by constraints (e.g. perpendicular, tangent) that define the relationships between them. For a design to be easily editable, the constraints must effectively capture design intent, ensuring the geometry updates predictably when parameters change. Although current approaches can generate CAD designs, an open challenge remains to align model outputs with design intent, we label this problem `design alignment'. A critical first step towards aligning generative CAD models is to generate constraints which fully-constrain all geometric primitives, without over-constraining or distorting sketch geometry. Using alignment techniques to train an existing constraint generation model with feedback from a constraint solver, we are able to fully-constrain 93% of sketches compared to 34% when using a na""ive supervised fine-tuning (SFT) baseline and only 8.9% without alignment. Our approach can be applied to any existing constraint generation model and sets the stage for further research bridging alignment strategies between the language and design domains.

Problem

• Aligning constraint generation with design intent in CAD
• Ensuring constraints fully-define geometry without over-constraining
• Improving constraint generation using alignment techniques from LLMs

Innovation

• Adapts LLM alignment for CAD constraint generation
• Ensures fully-constrained sketches without distortion
• Uses solver feedback to train generation model