CAD Sketch Constraint Sequencing to Avoid Overconstrained Solvers
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Solution Overview
Problem
Conventional constraint solvers in 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 unlabeled feature variants.
Innovation Solution
A computer-implemented method using a transformer-based architecture with a pointer network to generate constraint sequences by processing geometric entities, generating embeddings, and applying preference-based optimization and reinforcement learning to improve constraint generation accuracy and convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement 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
Solution Approach 1:
The patent changes the approach from inserting all identifiable constraints to selectively generating constraints based on learned geometric relationships. The machine learning model predicts which constraints are necessary based on the sketch's geometric features, transforming the constraint generation process from exhaustive to intelligent selection, thereby avoiding over-constraint while maintaining completeness.
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 a trained neural network to predict appropriate constraints, substituting the mechanical constraint satisfaction process with an AI-driven predictive model that understands geometric relationships.
2Device complexity
If traditional constraint solvers use heuristic techniques, then device complexity is reduced, but constraint accuracy deteriorates leading to unpredictable constraint sets
Solution Approach 1:
The patent replaces simple heuristic rules with a machine learning model that has learned complex geometric relationships from training data. The neural network processes geometric features and automatically determines appropriate constraints, substituting basic heuristic logic with an AI system that captures subtle geometric patterns and relationships.
Solution Approach 2:
The patent transforms the constraint generation approach from rule-based heuristics to data-driven predictions. The machine learning model uses learned parameters from training data to predict constraints, changing the basis of decision-making from explicit rules to implicit patterns discovered during training, thereby improving accuracy while maintaining manageable system complexity.
3Quantity of substance
If machine learning models rely on constrained or sparsely labeled training examples, then training data requirements are reduced, but constraint generation reliability deteriorates due to hidden correlations and unlabeled feature variants
Solution Approach 1:
The patent performs preliminary actions by pre-processing the training data to handle hidden correlations and unlabeled feature variants before they affect model training. The system uses techniques to identify and address data quality issues in advance, ensuring that the training process benefits from clean, well-structured data that captures true geometric relationships without being confounded by data artifacts.
Solution Approach 2:
The patent implements feedback mechanisms during training to continuously improve constraint generation reliability. The system uses feedback from constraint satisfaction results and geometric validation to refine the machine learning model, allowing it to learn from both successful and failed constraint generations, thereby improving reliability despite limitations in training data quality.
4Adaptability or versatility
If machine learning models are trained on incomplete or inconsistent datasets, then adaptability to diverse sketches is improved, but constraint generation accuracy deteriorates causing contradictory constraints
Solution Approach 1:
The patent changes the training approach to handle incomplete and inconsistent data by using techniques that robustly learn from varied data quality. The machine learning model uses parameter estimation methods that can handle missing or noisy information, allowing it to adapt to diverse sketch types while maintaining accuracy by learning patterns that are robust to data imperfections.
Solution Approach 2:
The patent applies beforehand cushioning by using training techniques that prepare the model for dealing with incomplete and inconsistent data. The system uses data augmentation, imputation, and robust loss functions during training to cushion against the effects of poor data quality, ensuring that the model remains accurate even when encountering diverse and imperfect real-world sketches.
Data Source
AI summary
Generative constraining and dimensioning of CAD sketches receiving an input sketch, the input sketch including geometric entities; processing the input sketch to determine one or more properties of each of the geometric entities, the one or more properties of a first geometric entity including a plurality of points along the first geometric entity, the points capturing a shape of the first geometric entity; generating embedded tokens from the properties of each of the geometric entities; generating contextualized geometry and constraint embeddings from the embedded tokens using a first transformer; gathering the contextualized geometry and constraint embeddings to generate a plurality of gathered constraints; processing the gathered constraints using a second transformer to generate pointers; and processing the pointers and the geometry and constraint embeddings using a pointer network to autoregressively generate a constraint sequence.