Semantic Data Extraction from CAD Prints for Manufacturing
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Solution Overview
Problem
Computer-aided drawings often fail to effectively extract and utilize semantic data from files like PDF, DXF, and DWG, which hinders the generation of accurate manufacturing instructions for various manufacturing processes.
Innovation Solution
A method and system that utilize a processor to receive a computer model and print of a part, extract semantic data from the print, map it onto the computer model, and generate manufacturing instructions, employing machine-learning processes to recognize and associate geometric dimensions and tolerances with corresponding components, even from non-extractable file types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional software is used to extract data from computer-aided drawings, then the existing software compatibility is maintained, but the semantic data extraction capability is insufficient
Solution Approach 1:
The patent introduces an intermediary component (image processing module and semantic recognition module) that bridges the gap between traditional CAD drawing formats and manufacturing instruction generation. This intermediary extracts semantic data from rendered images of CAD drawings, enabling data extraction from file types (PDF, DXF, DWG) that traditional software cannot process effectively, while maintaining compatibility with existing CAD workflows.
Solution Approach 2:
The patent replaces traditional mechanical data extraction methods (direct parsing of CAD file structures) with an image-based processing system. By rendering CAD drawings as images and using image processing algorithms to extract semantic information, the system can handle various file formats uniformly without requiring complex file format-specific parsers, thus reducing software complexity while improving extraction capability.
2Loss of information
If semantic data is extracted from non-extractable file types like PDF, then the data accessibility is improved, but the extraction accuracy may be compromised
Solution Approach 1:
The patent performs preliminary rendering of CAD drawings into high-quality images before extraction, ensuring that semantic data is captured in a standardized format. By pre-processing the drawings with proper scaling, resolution, and annotation preservation, the system maintains extraction accuracy even when working with non-extractable file types like PDFs, where direct data parsing would fail.
Solution Approach 2:
The patent creates accurate visual copies of CAD drawings in image format that preserve all semantic information. This copying approach allows the system to work with various file formats by converting them to a universal image representation, maintaining extraction accuracy through faithful reproduction of geometric dimensions, tolerances, and manufacturing annotations in the image copy.
3Measurement precision
If manual data extraction from prints is performed, then the extraction precision is maintained, but the manufacturing productivity is reduced
Solution Approach 1:
The patent implements an automated system that performs semantic data extraction without human intervention. The image processing module automatically identifies and extracts geometric dimensions, tolerances, and manufacturing annotations from rendered CAD drawings, eliminating the need for manual data entry while maintaining high extraction precision through advanced image recognition algorithms.
Solution Approach 2:
The patent transforms the extraction process from manual parameter measurement to automated image analysis. By changing the processing parameters from human-operated measurement tools to computer vision algorithms operating on image data, the system achieves both high precision (through consistent algorithmic application) and high productivity (through automated batch processing of multiple drawings).
Data Source
AI summary
A method for geometric analysis of a part for manufacture, the method including receiving, by a processor, a computer model of a part for manufacture; receiving, by the processor, a print of the part for manufacture; extracting, by the processor, a semantic datum from the print of the part for manufacture; mapping, by the processor, the semantic datum on the computer model of the part for manufacture; and generating, by the processor, a manufacturing instruction based on the semantic datum mapped on the computer model of the part for manufacture.