Coordinate Measurement Data Reduction via Feature Segmentation
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Solution Overview
Problem
Non-tactile coordinate measuring machines generate large amounts of point cloud data quickly, leading to inefficiencies in storage, communication, and processing, and existing data reduction techniques often compromise accuracy or precision when applied universally.
Innovation Solution
The method involves segmenting coordinate measurement data based on distinct geometric features identified from a CAD model, applying different data reduction techniques to each feature set according to specific requirements, such as type, tolerance, and other parameters, to reduce data in a targeted and strategic manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-tactile coordinate measuring machines scan workpiece surfaces to collect comprehensive measurement data, then measurement completeness and coverage are improved, but data volume and processing burden increase significantly
Solution Approach 1:
The patent segments the point cloud data into multiple data sets based on distinct geometric features of the workpiece (e.g., planes, cylinders, spheres). Each data set contains points corresponding to a specific feature type, allowing targeted processing and reduction of each segment independently rather than processing the entire large data set uniformly.
Solution Approach 2:
The patent extracts only the essential measurement information needed for quality inspection from the comprehensive point cloud data. By identifying and extracting key geometric features and their critical dimensions, the system reduces data volume while retaining the information necessary to determine part compliance.
2Productivity
If data reduction techniques are applied to reduce point cloud data volume, then processing efficiency and storage requirements are improved, but measurement accuracy and precision may be compromised
Solution Approach 1:
The patent applies different data reduction techniques and reduction ratios to different geometric features based on their specific characteristics and inspection requirements. For example, features with tight tolerances may undergo less aggressive reduction, while features with loose tolerances can be reduced more aggressively. This localized approach maintains measurement accuracy for critical features while achieving overall data reduction.
3Reliability
If comprehensive point cloud data is collected from all workpiece surfaces, then detection capability for manufacturing errors is improved, but data transmission and storage time increase
Solution Approach 1:
The patent performs preliminary segmentation and reduction of point cloud data immediately after acquisition, organizing data into feature-based data sets and applying appropriate reduction techniques before further processing or storage. This preliminary action reduces the data burden early in the workflow, preventing the accumulation and transmission of unnecessarily large data volumes while preserving detection capability.
Data Source
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AI summary
Coordinate measurement data such as point cloud data associated with coordinate measurement machine data is reduced in a strategic and systematic manner by segmenting and/or reducing data based on nominal geometric information contained in an electronic file such as a CAD model or a coordinate measurement machine inspection plan. For example, in one embodiment, a software application is used to identify geometric features and tolerances within a CAD model of an object, and to segment coordinate measurement data of a physical object based on the identified geometric features and tolerances from the CAD model. The various segments of coordinate measurement data may be assigned different data requirements, and the data may be reduced in different manners on a feature-by-feature basis.