CAD Sketch Constraint Sequencing for Reliable Dimensioning
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Solution Overview
Problem
Conventional constraint solvers in parametric CAD software often result in over-constrained or under-constrained conditions, leading to solver failures and unpredictable constraint sets, while machine learning models struggle with training data limitations and generate incorrect constraints due to hidden correlations and coverage gaps.
Innovation Solution
A computer-implemented method involving a constraint generation model trained using a scoring module to evaluate candidate sequences, combined with preference-based optimization and reinforcement learning, to generate accurate and convergent constraint sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional constraint solvers insert every identifiable constraint or dimension, then constraint completeness is improved, but solver reliability deteriorates due to over-constrained conditions or solver failures
Solution Approach 1:
The patent changes the approach from inserting all identifiable constraints to selectively generating constraints based on learned design patterns. The machine learning model predicts which constraints are most likely to be needed based on geometric relationships and design context, transforming the constraint generation process from exhaustive to intelligent selection.
Solution Approach 2:
The patent replaces traditional mechanical constraint solving approaches with machine learning-based constraint generation. Instead of using heuristic rules and partial ordering techniques, the system uses trained neural networks to predict and generate appropriate constraints, substituting the mechanical constraint satisfaction process with an AI-driven approach.
2Device complexity
If conventional constraint solvers rely on guesswork to decide which constraints are needed, then device complexity is reduced, but manufacturing precision deteriorates due to unpredictable or incomplete constraint sets
Solution Approach 1:
The patent replaces guesswork-based constraint selection with machine learning models that have learned from extensive training data. The neural networks analyze geometric relationships and design patterns to accurately predict which constraints are needed, eliminating the need for complex heuristic rules while improving constraint accuracy.
Solution Approach 2:
The patent transforms the constraint generation approach from simple guesswork to data-driven prediction. The machine learning model uses learned parameters from training data to accurately determine which constraints should be applied, achieving both simplicity in the system architecture and high accuracy in constraint generation.
3Quantity of substance
If machine learning models use constrained or sparsely labeled training examples, then training data requirements are reduced, but manufacturing precision deteriorates due to incorrect or misaligned constraints
Solution Approach 1:
The patent incorporates feedback mechanisms where the system evaluates generated constraints against the original design intent and geometric relationships. This feedback loop allows the model to learn from its mistakes and refine its constraint generation, ensuring high accuracy even when training data is limited or imperfectly labeled.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and curating training data to ensure quality before training the model. The system prepares labeled examples that capture accurate geometric relationships and design patterns, so that when the model generates constraints, they are already aligned with correct geometric relationships, reducing the need for extensive training data.
Data Source
AI summary
Generative constraining and dimensioning of CAD sketches includes generating one or more candidate constraint sequences using a constraint generation model, generating one or more quality scores for each of the candidate constraint sequences, and performing alignment training on the constraint generation model based on the one or more quality scores and the one or more candidate constraint sequences.