3D RGB Grain Mapping for Turbine Component Inspection
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Solution Overview
Problem
The inspection of single crystal turbine components for secondary grains is labor-intensive and prone to errors due to the reliance on human visual assessment, which impacts performance standards.
Innovation Solution
An automated method and system using RGB light scanning and structured light to create a combined 3D model with an RGB light overlay, analyzing changes in color and contrast to identify grain boundaries, which can be mapped and reported.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human visual inspection is used to identify grain boundaries, then the inspection can be performed with simple equipment, but the inspection is labor-intensive and prone to errors
Solution Approach 1:
The patent replaces the mechanical human visual inspection system with an automated optical scanning system using RGB light sources and cameras. The system captures images from multiple angles and uses image processing algorithms to automatically identify grain boundaries, eliminating manual inspection while improving both accuracy and efficiency.
Solution Approach 2:
The patent creates a digital representation (3D model with texture mapping) of the component surface that replicates the visual appearance under different lighting conditions. This digital copy allows for repeated analysis without physical re-inspection and enables automated detection algorithms to identify grain boundaries with consistent accuracy.
2Measurement precision
If automated RGB light scanning is used to identify grain boundaries, then inspection speed and accuracy are improved, but the device complexity increases
Solution Approach 1:
The patent employs a multi-functional scanning system where a single apparatus performs both 3D geometric scanning and RGB color texture capture. The same camera system and lighting setup used for visual inspection are leveraged to create comprehensive surface models, eliminating the need for separate specialized equipment for each measurement type.
Solution Approach 2:
The patent introduces software-based image processing and 3D modeling algorithms as intermediaries between the physical scanning process and grain boundary detection. These computational tools process the raw RGB scan data, apply color space transformations, and enhance contrast to automatically reveal grain boundaries without requiring complex hardware modifications.
3Reliability
If multiple views are analyzed to identify grain boundaries through color and contrast changes, then detection accuracy is improved, but the analysis time increases
Solution Approach 1:
The patent performs preliminary processing of the RGB scan data by creating a 3D model with texture mapping and pre-calculating color information from multiple viewing angles. This preliminary organization of data in a structured format enables faster subsequent analysis and grain boundary detection, as the computational work of rendering multiple views is already completed during data acquisition.
Solution Approach 2:
The patent implements continuous image processing and analysis algorithms that operate on the RGB scan data in real-time or near-real-time. Rather than analyzing multiple views sequentially, the system processes all viewing angle information simultaneously through efficient algorithms, maintaining continuous useful action throughout the analysis phase and minimizing total inspection time.
Data Source
AI summary
A method for grain mapping of a surface of a component includes the steps of constructing a model comprising a three-dimensional scanned model of the component; scanning the component with RGB light to obtain an RGB light scan; applying the RGB light scan to the three-dimensional scanned model of the component to produce a combined three-dimensional model of the component with an RGB light scan overlay on the three-dimensional model; and analyzing the combined three-dimensional model of the component with the RGB light scan overlay on the three-dimensional model to identify grain boundaries in the component.