Topological Surface Detector for Mesh Component Identification
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Solution Overview
Problem
Current methods for identifying geometric components in discretized mesh models are manual, time-consuming, and require significant effort, especially when design changes occur, as they rely on predefined ID systems that become inconsistent with updates, limiting their scalability and efficiency in analyzing complex vehicle designs.
Innovation Solution
A method that constructs an adjacency graph based on face connections, assigns similarity metrics using normal vectors, and employs graph pruning to identify strongly connected components, allowing for automatic detection of geometric surfaces without relying on predefined axis systems or ID patterns, thus enabling rapid analysis of updated designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification methods using predefined ID systems are used, then geometric components can be identified, but the process is time-consuming and requires significant effort
Solution Approach 1:
The system performs self-identification of geometric surfaces by automatically analyzing mesh topology and computing surface properties without requiring external manual input or predefined ID systems. The algorithm autonomously traverses the mesh, identifies surface boundaries, and generates surface IDs based on geometric characteristics.
Solution Approach 2:
The manual mechanical process of exploring and documenting mesh components is replaced by an automated computational algorithm that uses topological analysis and geometric property calculation to identify surfaces, eliminating the need for manual intervention while maintaining identification accuracy.
2Ease of operation
If predefined ID systems are used for mesh components, then identification is straightforward, but the system becomes inconsistent when design changes occur
Solution Approach 1:
The identification system is made dynamic by automatically recalculating surface properties and re-identifying geometric components whenever the mesh changes. The algorithm adapts to design modifications by re-analyzing the updated mesh topology and geometric properties, ensuring consistent identification regardless of design evolution.
Solution Approach 2:
Instead of relying on static predefined ID systems, the invention uses dynamic parameter-based identification where surfaces are characterized by their geometric properties (area, volume, normal vectors, curvature). These parameters are recomputed when design changes occur, maintaining identification consistency without requiring predefined ID conventions.
3Measurement precision
If manual exploration of mesh is performed to identify geometric components, then specific parts can be found, but the process does not scale well with large meshes
Solution Approach 1:
The mesh analysis process is segmented into distinct computational stages: topological analysis to identify connectivity, geometric property calculation for each element, surface boundary detection, and component classification. This segmentation allows the algorithm to efficiently process large meshes by breaking down the complex identification task into manageable computational steps.
Solution Approach 2:
The manual exploration process is replaced by an automated computational system that uses algorithmic mesh traversal and geometric property calculation to identify components. This substitution enables the system to scale efficiently to large meshes by leveraging computational algorithms rather than manual inspection methods.
4Ease of operation
If predefined ID conventions are used for mesh faces, then identification can be performed, but the method requires significant manual effort and documentation
Solution Approach 1:
The system generates its own identification framework by automatically computing geometric properties and deriving surface IDs from the mesh data itself. No external predefined ID conventions or manual documentation processes are required - the system self-identifies all geometric components and outputs the results in a structured format ready for analysis.
Data Source
AI summary
A method of identifying surfaces within a discretized mesh model is provided. The method comprises identifying a number of faces in the mesh model and constructing an adjacency graph of connections between the faces. A value is assigned to each connection in the adjacency graph according to a metric of similarity between incident faces of the connection. Connections with a metric of similarity value that satisfies a prescribed policy of elimination are removed from the adjacency graph. From the remaining connections in the adjacency graph a number of strongly connected components in the mesh model are determined.