Monocrystalline Component Defect Classification via Optical Contrast
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Solution Overview
Problem
Existing methods for classifying defects in turbine blades with monocrystalline structures are costly, time-consuming, and lack the ability to distinguish between different types of defects, requiring significant operator input and often relying on costly X-ray crystallography for confirmation.
Innovation Solution
A method involving illumination of a turbine blade surface with light from multiple directions, measuring reflected intensity, and analyzing contrast values through a series of tests to differentiate between secondary grains and scratches, using a computer-implemented analysis algorithm to determine the type and severity of defects based on scoring criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray crystallography is used to examine crystal structure and identify secondary grains, then measurement precision is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical/X-ray based crystallography inspection system with an optical inspection system using light sources and cameras. The system uses optical reflection patterns to identify secondary grains, substituting complex and time-consuming X-ray crystallography with a faster optical method that maintains sufficient detection capability for defect identification.
Solution Approach 2:
The patent creates optical copies (reflected light patterns) of the crystal structure surface features to identify defects. By analyzing how light reflects off the turbine blade surface at different angles, the system generates visual representations that reveal secondary grain patterns without requiring physical or radiological examination of the actual crystal structure.
2Loss of time
If visual inspection techniques are used to identify secondary grains, then loss of time is reduced, but ease of operation worsens due to requiring significant operator input and inability to easily distinguish between different defect types
Solution Approach 1:
The patent implements an automated analysis system that performs defect classification independently without requiring operator intervention. The computer system automatically processes the optical images, applies analysis algorithms, and classifies defects into categories (secondary grains, scratches, or other defects) based on characteristic patterns, making the system self-sufficient and eliminating the need for skilled operator judgment.
Solution Approach 2:
The system incorporates automated feedback mechanisms where the computer system continuously analyzes the optical data, compares it against known defect patterns, and provides automatic classification results. This feedback loop enables the system to self-correct and maintain consistent defect identification without operator input, transforming manual visual inspection into an automated decision-making process.
3Loss of time
If existing visual inspection techniques are used, then loss of time is reduced compared to X-ray, but measurement precision worsens due to inability to reliably distinguish between different defect types
Solution Approach 1:
The patent segments the defect identification process into multiple distinct analytical tests that evaluate different characteristics of the reflected light patterns. By dividing the analysis into separate tests (each examining specific features like contrast ratios, peak positions, or pattern geometries), the system can accurately distinguish between different defect types while maintaining rapid inspection speeds.
Solution Approach 2:
The system dynamically adjusts its analysis approach by performing multiple sequential tests on the optical data and using scoring mechanisms to determine defect classification. This dynamic, multi-stage analysis process allows the system to adapt to different defect types and conditions, improving classification accuracy compared to static visual inspection methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a more repeatable and informative technique for defect classification, reducing the need for costly X-ray analysis and enabling automated differentiation between secondary grains and scratches, with the potential to identify ultra-high angle grain defects and quantify scratch severity.
Implementation Method 1
illuminating a surface of a component containing a defect with a beam of light from a plurality of different spherical directions centred on the surface; for each illumination direction, measuring the intensity of light reflected by the surface and received by a detector
Data Source
AI summary
Method and apparatus for classifying defect in component having a monocrystalline structure. The method includes: illuminating surface of component containing defect with beam of light from plurality of different spherical directions; each illumination direction, measuring intensity of light reflected by surface and received by detector; determining contrast value between region with higher intensity and a region with lower intensity for each illumination direction; analyzing contrast values by performing tests selected from the following: (a) determining whether region with higher intensity exceeds predetermined width; (b) identifying illumination direction which produces maximum contrast value, and determining whether illumination direction falls outside of predetermined region; (c) identifying peak in contrast values and determining whether peak extends over range of illumination directions which exceeds predetermined threshold; and (d) determining whether contrast values contain plurality of discontinuous peaks; and determining type of defect based on of tests.