Technical Drawing Graph Reconstruction for Geometry-Annotation Linking
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Solution Overview
Problem
Existing methods for processing technical drawings are inefficient in detecting and reconstructing technical data, such as geometry and annotations, from their images, limiting their use in manufacturing and defect detection.
Innovation Solution
A computer-implemented method that constructs a graph representation of a technical drawing image, clustering nodes and edges to reconstruct geometries and annotations, using machine-learning to identify and associate technical data for numerical model creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to detect technical data in drawing images, then the processing can be performed with simple algorithms, but the detection precision and reconstruction accuracy are insufficient
Solution Approach 1:
The patent replaces traditional mechanical image processing algorithms with a neural network-based system. The neural network automatically learns and extracts technical data features from drawing images, substituting manual feature detection and classification with intelligent algorithms that achieve higher precision without requiring complex manual processing steps
Solution Approach 2:
The patent transforms the processing approach by changing from fixed threshold-based detection to adaptive neural network parameter adjustment. The system dynamically adjusts detection parameters based on learned patterns, improving precision while the neural network handles the complexity internally, presenting a simplified interface to users
2Productivity
If manual processing methods are used for technical drawing data extraction, then the processing steps are simple and understandable, but the productivity is low and time-consuming
Solution Approach 1:
The neural network system performs self-learning and automatic feature extraction without requiring manual intervention at each processing step. The system automatically detects geometries, dimensions, and tolerances, and reconstructs technical data, eliminating the need for manual processing while significantly improving productivity and reducing time loss
Solution Approach 2:
The system performs preliminary learning and feature extraction automatically during the processing pipeline. By pre-training the neural network on technical drawing patterns, the system prepares the detection models in advance, enabling rapid processing of new drawings without manual setup, thus improving overall processing efficiency
3Manufacturing precision
If simple clustering methods are used for geometry reconstruction, then the algorithm is easy to implement, but the reconstruction accuracy of geometries and annotations is insufficient
Solution Approach 1:
The patent replaces simple clustering algorithms with a neural network-based reconstruction system. The neural network learns the complex relationships between detected features and their spatial relationships, automatically reconstructuring geometries and annotations with high accuracy. The network handles the algorithmic complexity internally while providing accurate reconstruction results
Solution Approach 2:
The reconstruction system combines multiple neural network components and processing stages into a composite system. By integrating feature detection, classification, and reconstruction modules with different specialized functions, the system achieves high manufacturing precision through the synergistic combination of multiple intelligent processing elements
4Measurement precision
If automated neural network processing is implemented for technical data extraction, then the productivity and precision are improved, but the device complexity and implementation difficulty increase
Solution Approach 1:
The neural network system is designed as a universal platform that handles multiple tasks including geometry detection, dimension extraction, tolerance identification, and technical annotation reconstruction. By creating a multi-functional system that performs all these operations through a single integrated architecture, the patent improves precision across all tasks while avoiding the need for multiple separate complex systems
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges the neural network's complex internal operations and the user's simple interface. This intermediary layer handles feature extraction, classification, and reconstruction automatically, shielding users from the underlying complexity while delivering high-precision results through the neural network's intelligent processing
Data Source
AI summary
A graph processing method where the graph represents an image of a technical drawing including a view and a technical annotation. The method includes, for each view, providing the graph. The graph including nodes and edges. Each node corresponds to a classification of pixels into a semantic class of a predetermined set. Each edge connects two nodes either if the two nodes represent neighboring pixels or if the two nodes represent pixels distant from each other below a predetermined threshold. The set includes geometry, dimension, dimension-related symbol. The method includes clustering, based on the graph topology: nodes corresponding to the geometry class, to reconstruct the geometries in the view, and nodes corresponding to the dimension and dimension-related symbol classes, to reconstruct the annotations of the view. The method includes, associating reconstructed annotations to reconstructed geometries based on a detected position of the annotations and on the graph topology.