B-Rep Through Pocket Recognition for Multi-Level Feature Loops
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods fail to efficiently identify multi-level through pocket features in Boundary Representation (B-Rep) models due to the assumption of a known axis and the complexity of face-edge graph processing, especially in drafted scenarios and features bounded by multiple cycles.
Innovation Solution
A method and system that identify through pocket features by starting with a reference shell face and sequentially identifying consecutively adjacent shell faces and edges, using reference and subsequent identification criteria, and validating the formed loop based on specific criteria to determine valid through pocket features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If face-edge graph generation and classification techniques are used to identify pocket features, then recognition coverage is improved, but device complexity and computational burden increase significantly
Solution Approach 1:
The method segments the pocket feature identification process into distinct phases: identifying entrance faces, determining pocket axes, classifying face relationships, and validating features. This segmentation reduces computational complexity by processing only relevant faces and edges at each stage rather than analyzing the entire face-edge graph.
Solution Approach 2:
The method performs preliminary identification of entrance faces and pocket axes before proceeding to detailed feature classification. By pre-identifying these key elements, the algorithm reduces the search space for subsequent processing steps, thereby reducing overall computational burden while maintaining recognition accuracy.
2Ease of operation
If pocket feature identification assumes a known axis, then identification process is simplified, but adaptability to complex drafted scenarios is reduced
Solution Approach 1:
The method dynamically determines pocket axes based on the geometric relationships between identified entrance faces and shell faces, rather than assuming a fixed or pre-known axis. This dynamic adaptation allows the algorithm to handle drafted scenarios and complex geometries while maintaining a systematic identification process.
Solution Approach 2:
The algorithm changes the axis parameter adaptively during processing by calculating it from the normal vectors of entrance faces and their spatial relationships. This parameter transformation enables the method to accommodate various pocket orientations and drafted scenarios without requiring manual axis specification.
3Reliability
If face-edge graph processing is used to identify through pockets, then feature recognition is improved, but processing time and computational resources increase
Solution Approach 1:
The method extracts and processes only the essential elements (entrance faces, shell faces, and their connecting edges) required for through pocket identification, rather than processing the entire face-edge graph. This extraction approach maintains feature recognition accuracy while significantly reducing processing time by eliminating redundant computations.
Solution Approach 2:
The algorithm performs partial processing by focusing only on the subset of faces and edges that are relevant to through pocket features, using validation criteria to filter out non-relevant elements. This partial action approach achieves sufficient recognition accuracy without the computational overhead of complete graph processing.
4Reliability
If existing techniques are used to identify pocket features, then simple features are recognized, but multi-level through pocket features with non-unique entrance faces are missed
Solution Approach 1:
The method employs a universal identification framework that handles both simple and complex multi-level through pocket features using the same core algorithm. By using validation criteria based on shell face relationships and loop formation, the system achieves multi-functionality in recognizing diverse pocket feature types without requiring separate specialized procedures.
Solution Approach 2:
The algorithm extends feature identification into additional dimensional considerations by analyzing multi-level shell face relationships and three-dimensional loop formations, rather than relying solely on two-dimensional face-edge graph representations. This dimensional extension enables accurate identification of complex multi-level through pockets with non-unique entrance faces.
Data Source
AI summary
This disclosure relates to method and system for identifying through pocket features having a non-unique entrance face from B-Rep models. The method includes receiving a user input including a B-Rep model. The method further includes identifying a reference shell face from the plurality of faces and an associated reference shell edge based on reference identification criteria. The reference shell face includes the reference shell edge. The method further includes sequentially identifying a set of consecutively adjacent shell faces from remaining of the plurality of faces and a corresponding set of shell edges based on subsequent identification criteria. The method further includes validating a loop formed by the reference shell face and the set of consecutively adjacent shell faces based on through pocket validation criteria.