2D Print Feature Recognition for Manufacturing
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Solution Overview
Problem
Computer-aided drawings often fail to effectively convey information necessary for manufacturing processes, as existing software struggles to extract relevant geometric and topological features from two-dimensional prints.
Innovation Solution
A processor-based system that receives, scales, and identifies curve features in two-dimensional prints, classifying them into line types using machine-learning models to extract semantic data for manufacturing instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing software is used to extract information from computer-aided drawings, then the software operation is simple, but the information extraction capability is insufficient
Solution Approach 1:
The system segments the information extraction process into distinct modules: curve feature identification, line type classification, and manufacturing instruction generation. Each module handles specific aspects of feature recognition, improving overall information extraction capability while maintaining manageable complexity through functional decomposition
Solution Approach 2:
The patent introduces an intermediary processing layer between the computer-aided drawing and the manufacturing system. This intermediary system performs curve feature identification and line type classification, transforming raw drawing data into structured manufacturing instructions that both simple software and complex manufacturing requirements can utilize
2Measurement precision
If curve features are identified as a function of scaling, then the measurement precision is improved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary scaling of the two-dimensional print before curve feature identification. By pre-scaling the drawing to a standard format, the system simplifies subsequent feature detection operations while maintaining measurement precision, as the scaling relationship is established beforehand and used as a reference for all measurements
Solution Approach 2:
The patent changes the parameter of drawing scale as a function of which curve features are being identified. By dynamically adjusting scale parameters based on the specific curve features being analyzed, the system achieves high measurement precision while managing computational complexity through targeted parameter optimization rather than processing all features at maximum detail
3Manufacturing precision
If machine-learning models are used for line type classification, then the classification accuracy is improved, but the processing time increases
Solution Approach 1:
The classification process is segmented into curve feature identification followed by line type classification. This segmentation allows the system to first identify relevant geometric features, then apply machine-learning models only to classify the line types of identified features, rather than processing all line data through complex classification algorithms
Solution Approach 2:
The system performs preliminary curve feature identification before applying machine-learning models for line type classification. By pre-identifying curve features and their geometric properties, the system reduces the input data size and complexity for the machine-learning classification stage, improving processing speed while maintaining classification accuracy
Data Source
AI summary
An apparatus for feature recognition of two-dimensional prints is illustrated. The apparatus comprise a processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a two-dimensional print of a part for manufacture, scale two-dimensional print so that the two-dimensional print is within a predetermined area, identify a curve feature of the two-dimensional print as a function of scaling of the two-dimensional print, wherein the curve feature comprises a plurality of line segments, and classify a line type of the curve feature using line observations as a function of the curve feature identification.