Selective CAD Object Incarnation for Complex 3D Geometry
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Solution Overview
Problem
Existing CAD systems face inefficiencies in processing complex 3D geometries due to the limitations of boundary representation models, which require significant memory and computational resources when modeling intricate internal and external structures, especially with programmatic representations involving millions of geometric elements.
Innovation Solution
Implementing a machine learning-based selective incarnation system that identifies and incarnates only relevant subsets of pattern instances for specific CAD operations, using an inversion ML model to determine a selected subset of pattern instances applicable to a given operation, thereby reducing memory and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If boundary representation models are used to model complex 3D geometries with millions of geometric elements, then the ability to represent intricate internal and external structures is improved, but memory usage and computational resources increase significantly
Solution Approach 1:
The patent segments the complex 3D geometry into multiple pattern instances that can be processed independently. Instead of loading all geometric elements into memory simultaneously, the system divides the geometry into manageable segments (pattern instances) and processes them selectively based on the CAD operation being performed.
Solution Approach 2:
The patent implements partial incarnation by only converting and processing the subset of pattern instances that are relevant to the current CAD operation, rather than incarnating the entire geometry. This partial action approach reduces memory usage by avoiding the conversion and storage of unnecessary geometric elements.
2Manufacturing precision
If all pattern instances are incarnated for CAD operations, then complete geometric accuracy is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by determining which specific pattern instances are relevant to the current CAD operation and only incarnating those local subsets. Different regions of the geometry receive different treatment - only the relevant portions are fully processed while others remain in compact representation, optimizing both accuracy and performance.
Solution Approach 2:
The system performs partial incarnation by selectively converting only the necessary pattern instances for the current operation rather than all pattern instances. This partial action maintains geometric accuracy where needed while avoiding the computational overhead of processing the entire geometry.
3Quantity of substance
If programmatic patterns are used to represent geometries, then memory efficiency is improved, but the ability to perform CAD operations requires full incarnation which increases complexity
Solution Approach 1:
The patent implements a dynamic system that adapts between programmatic and incarnated representations based on operational needs. The system dynamically determines which pattern instances should be incarnated for specific CAD operations, creating a flexible hybrid approach that balances memory efficiency with operational capability without requiring full incarnation.
Solution Approach 2:
The patent introduces an intermediary mechanism (the instance identification engine and inversion ML model) that bridges the gap between compact programmatic patterns and full geometric incarnation. This intermediary selectively identifies and processes only the necessary pattern instances, reducing the complexity of full incarnation while maintaining the ability to perform CAD operations.
Data Source
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AI summary
A computing system may include an instance identification engine configured to determine a selected subset of pattern instances of a programmatic pattern used to represent a geometry of a computer-aided design (CAD) object, including by identifying a CAD operation to perform on the CAD object; determining a sampled point set in the CAD object applicable to the CAD operation; providing the sampled point set as an input to an inversion machine-learning (ML) model trained to output a given pattern instance of the programmatic pattern for an input point of the CAD object; and determining, as the selected subset, an output set of pattern instances provided by the inversion ML model for the sampled point set. The system may also include an object incarnation engine configured to incarnate a geometry of the selected subset of pattern instances to perform the CAD operation on the CAD object.