3D Skeleton Feature Comparison for Additive Manufacturing Distortion
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Solution Overview
Problem
Conventional spatial difference measurement methods in additive manufacturing are unreliable and inaccurate due to reliance on manual selection of reference points, leading to significant variations in measurement results.
Innovation Solution
The method generates key features from the skeletons of nominal and actual 3D models, projecting them onto the models' surfaces for comparison, allowing for real-time measurement and adjustment of additive manufacturing processes to account for spatial differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual reference points are selected for spatial difference measurement, then the measurement process is simple to operate, but the measurement precision and reliability deteriorate due to significant variations in results
Solution Approach 1:
The patent creates a digital skeleton model that copies and represents the essential structural features of the physical object. This digital skeleton serves as a standardized reference that can be consistently reproduced and compared, eliminating the need for manual reference point selection while maintaining measurement accuracy.
Solution Approach 2:
The patent transforms the measurement approach by changing from manual coordinate selection to automated skeleton-based feature extraction. The skeleton model parameters (nodes, edges, connectivity) provide an objective basis for measurement that is invariant to manual selection variations, thereby improving measurement precision.
2Ease of manufacture
If traditional surface point comparison methods are used, then the measurement process is straightforward, but the reliability deteriorates due to heavy reliance on reference choice
Solution Approach 1:
The patent extracts the essential structural information from the complete surface model by creating a simplified skeleton representation. This skeleton contains only the critical geometric features needed for measurement, removing the influence of surface variations and manual reference selection while maintaining measurement reliability.
Solution Approach 2:
The patent segments the complex surface comparison problem into discrete skeleton nodes and edges. By comparing specific skeleton features rather than continuous surface points, the method achieves both simplicity and reliability through structured, discrete feature matching.
3Use of energy by moving object
If conventional measurement methods are applied, then the process requires minimal computational resources, but the measurement accuracy deteriorates due to sensitivity to reference point selection
Solution Approach 1:
The patent performs preliminary processing by generating the skeleton model and identifying key features before the actual measurement comparison. This pre-processing creates a standardized reference framework that enables accurate measurements without requiring intensive computational resources during the comparison phase.
Data Source
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AI summary
A spatial difference measurement method includes generating first key features (301) of a first skeleton (300) of a nominal 3D model (200) of an object and extrapolating the first key features onto the nominal 3D model. The method includes creating an actual 3D model (500) of the object during or after a construction process (real or simulated). The method includes generating second key features (601) of a second skeleton (600) of the actual 3D model of the object and extrapolating the second key features onto the actual 3D model of the object. The method includes comparing the first key features extrapolated on the nominal 3D model to the second key features extrapolated on the actual 3D model to determine one or more distances between the first and second key features to measure a spatial difference between the nominal 3D model and the object during or after construction.