Thermal Weld Inspection Using Binary Image Noise Reduction
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Solution Overview
Problem
Traditional weld inspection methods are not 100% effective, often require destructive testing, are time-consuming, and are sensitive to thermal reflection, especially when dealing with low emissivity materials, which complicates the use of thermographic technology.
Innovation Solution
A computer software product and weld inspection system that uses a processor to transform raw thermal images into binary images, applying mathematical operators and modules like HSD, saturation, and roughness to reduce noise and improve image quality, allowing for non-destructive and robust weld inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional thermographic methods are used to inspect welds, then inspection can be performed non-destructively, but the system becomes highly sensitive to thermal reflection from low emissivity materials, creating noise in thermal images
Solution Approach 1:
The system applies periodic heating cycles to the work product, capturing thermal images at multiple time points during each cycle. By periodically heating and imaging at different phases, the system captures transient thermal behavior that distinguishes weld defects from reflective noise, allowing reliable non-destructive inspection of low emissivity materials without painting
Solution Approach 2:
The system performs preliminary heating of the work product before inspection, establishing a controlled thermal state. This preliminary thermal excitation ensures that the material reaches a stable thermal condition where reflection effects are minimized and weld characteristics become more pronounced in the thermal images
2Measurement precision
If multiple image processing modules are applied to reduce noise, then image quality improves, but the complexity of the inspection system increases
Solution Approach 1:
The image processing system is divided into distinct modular components: a first module that transforms raw thermal images into binary images using thresholding, and a combination module that integrates multiple binary images through logical operations. This segmentation allows each module to perform a specific function efficiently, improving image quality while maintaining manageable system complexity through clear functional separation
Solution Approach 2:
The combination module merges multiple binary images captured during different phases of the heating cycle using logical combination operations. By combining images from multiple time points, the system reinforces consistent weld features while eliminating transient noise, thereby improving measurement precision through data integration
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
The system provides a robust and efficient method for inspecting welds by reducing noise and improving image quality, enabling accurate assessment of welds on materials with low emissivity without the need for painting or destructive testing.
Implementation Method 1
a heat source assembly adapted to sequentially direct first and second heat pulses upon the work product
Implementation Method 2
a thermal imaging camera adapted to generate first and second binary images of the work product during the first and second time durations, respectively
Data Source
AI summary
A computer software product adapted for use in a weld inspection system is executed by a processor and is stored in an electronic storage medium of the weld inspection system adapted to facilitate the inspection of a weld of a work product. The computer software product includes a first module and a combination module. The first module is configured to transform first and second raw thermal images, associated with respective first and second heat pulses of at least a portion of the work product having the weld, into respective first and second binary images. The combination module is configured to transform the first and second binary images into a combined binary image for the reduction of noise.