3D CAD Search Key Shape Registration Through Feature Clustering
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Solution Overview
Problem
Existing design support systems struggle to efficiently identify and register search key shapes for dedicated shapes in three-dimensional CAD data, requiring trial and error and skilled labor, especially when dealing with multifaceted structures like boss structures, leading to potential design rule omissions and inefficiencies.
Innovation Solution
A design support device and method that utilizes a processor, memory, and a search key shape registration program to automatically generate search key shapes by clustering feature amounts of three-dimensional CAD data, allowing for efficient identification of dedicated shapes through a similar shape search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial and error method is used to define search key shapes, then recognition accuracy can be improved, but time consumption and labor cost increase significantly
Solution Approach 1:
The system performs preliminary clustering analysis on three-dimensional CAD data to pre-identify candidate shapes that match dedicated shape characteristics. By preparing and organizing candidate shapes in advance using automated clustering algorithms, the system reduces the need for manual trial and error while maintaining high recognition accuracy when designers select from pre-processed options.
Solution Approach 2:
The system enables self-service by automatically generating search key shapes through clustering analysis without requiring manual intervention. The clustering unit autonomously processes CAD data, identifies candidate shapes, and generates search key shapes based on cluster centers, allowing the system to serve itself rather than relying on skilled designers to define each search key shape manually.
2Productivity
If automated clustering method is used to generate search key shapes, then productivity is improved, but recognition accuracy may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where clustering results are evaluated and refined. The clustering unit generates initial search key shapes, which are then used in similar shape searches to validate effectiveness. Based on search results and design rule check outcomes, the system can adjust clustering parameters and re-generate search key shapes, ensuring both high productivity and maintained recognition accuracy through iterative improvement.
Solution Approach 2:
The system dynamically adjusts clustering parameters such as cluster number, distance thresholds, and feature weightings to optimize both efficiency and accuracy. By changing parameters based on data characteristics and search requirements, the automated method maintains recognition accuracy comparable to manual methods while achieving significantly higher productivity in generating search key shapes.
3Reliability
If dedicated shapes with multifaceted structures are processed manually, then design rule check coverage is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of dedicated shape identification into manageable components: feature amount extraction, clustering analysis, candidate selection, and search key shape generation. By dividing the processing into discrete functional units, the system can handle multifaceted structures effectively while keeping each component relatively simple and maintainable.
Solution Approach 2:
The clustering-based search key shape generation system provides universal functionality that can handle various types of dedicated shapes with multifaceted structures (boss structures, reinforcing structures, receiving structures, etc.) using the same automated process. This multi-functional approach eliminates the need for separate specialized tools for each shape type, reducing overall system complexity while maintaining comprehensive design rule check coverage.
Data Source
AI summary
Generation of a search key shape for identifying a dedicated shape with respect to three-dimensional CAD data is facilitated. A shape group registration unit registers multiple pieces of three-dimensional CAD data; a feature amount computation unit computes a feature amount with respect to each of the registered pieces of three-dimensional CAD data; a clustering unit clusters the pieces of three-dimensional CAD data based on a feature amount; a cluster center computation unit determines a feature amount of a cluster center of each cluster from a feature amount of three-dimensional CAD data; and a search key shape registration unit registers feature amount data of a cluster center as feature amount data of a search key shape.