Thermography Contrast Analysis for Composite Delamination Detection
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Solution Overview
Problem
Current infrared thermography methods for nondestructive evaluation face challenges in accurately detecting and characterizing delaminations in composite materials, particularly in distinguishing between near-surface and deep anomalies due to limitations in data processing and analysis techniques.
Innovation Solution
The development of three contrast video image processing methods: Normalized Contrast and Derivatives (NCD), Converted Contrast and Derivatives (CCD), and Normalized Temperature Contrast and Derivatives (TCD), which involve calculating and analyzing derivatives of pixel intensity and temperature changes to enhance anomaly detection and characterization, along with a calibration method for depth evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional infrared thermography data processing methods are used, then the basic detection of anomalies is possible, but the ability to accurately distinguish between near-surface and deep anomalies is insufficient
Solution Approach 1:
The patent segments the thermography data into multiple contrast video sequences, each representing different time frames or processing stages. By dividing the data into discrete temporal segments and analyzing derivatives at each segment, the system achieves more precise depth assessment while reducing the complexity of analyzing the entire continuous signal at once.
Solution Approach 2:
The patent introduces temporal dimensionality by processing contrast video sequences and calculating derivatives with respect to time. This transforms spatial temperature distributions into temporal-contrast representations, enabling the system to distinguish between near-surface and deep anomalies based on their different thermal response rates, thereby improving depth assessment accuracy.
2Reliability
If raw pixel intensity data is used directly, then the data processing is simple, but noise reduces detection reliability
Solution Approach 1:
The patent implements feedback mechanisms through iterative processing where contrast video sequences are generated, analyzed, and used to refine subsequent processing steps. The derivative calculations provide feedback about thermal response characteristics, enabling the system to enhance signal reliability while managing processing complexity through structured iterative algorithms.
Solution Approach 2:
The patent transforms raw pixel intensity data into normalized contrast values and temperature contrasts, changing the data parameters to enhance signal-to-noise ratio. By normalizing against reference regions and converting to temperature-based contrasts, the system improves detection reliability while the systematic parameter transformation keeps processing complexity manageable.
3Measurement precision
If multiple contrast video processing methods are implemented, then anomaly detection and characterization improve, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating contrast video sequences and storing them for later analysis. By preparing processed data in advance and organizing it into structured sequences, the system reduces real-time processing requirements while maintaining high anomaly detection accuracy, thereby managing the time-cost trade-off.
Solution Approach 2:
The patent applies partial action by selectively processing and analyzing only the most informative portions of the thermography data. Through derivative calculations and contrast normalization, the system identifies and focuses analysis on regions and time frames that provide maximum diagnostic value, reducing overall processing time while maintaining detection precision.
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
These methods improve the detection and characterization of anomalies by providing enhanced contrast and temperature data analysis, reducing noise, and enabling more accurate depth assessment of delaminations, thereby improving the reliability of infrared thermography in nondestructive evaluation.
Implementation Method 1
an infrared camera for capturing video images
Implementation Method 2
The IR camera captures a sequence of images of the surface temperature
Implementation Method 3
a heat lamp (source of light/heat)... For flash thermography, data acquisition uses an intense flash of light from a heat lamp
Data Source
AI summary
Methods and systems for analyzing and processing digital data comprising a plurality of infra-red (IR) video images acquired by thermography system are used to compute video data from the raw and smoothed video data acquired for the performance of non-destructive evaluation. New video data types computed may include but are not limited to contrast evolution data such as normalized contrast, converted contrast and normalized temperature contrast. Additionally, video data types computed comprise surface temperature, surface temperature rise and temperature simple contrast.