研究目的
To enhance the image processing and archiving capabilities of a commercial magneto-optical imaging system for non-destructive evaluation by addressing issues of noise, lack of recordable results, and data post-processing, and to implement automatic flaw detection techniques.
研究成果
The add-ons developed for the MOI system successfully provided digital recording, image stitching, and advanced image processing capabilities, improving the interpretation, archiving, and reporting of inspection results. Histogram adjustment and background subtraction were the most effective techniques for enhancing flaw visibility, but further refinements are needed to handle imager-specific variations and ensure all features are detected.
研究不足
The background magnetic domains in the garnet film are not stable and change over time, reducing the efficiency of background subtraction during scans. Variations between different MOI imagers (e.g., 303 and 307) require adjustments in processing parameters, and there is a risk of missing small features of interest in automated detection.
1:Experimental Design and Method Selection:
The study involved developing hardware and software add-ons to a commercial MOI system to enable digital recording, image stitching, and advanced image processing. Methods included digitizing video signals, using encoders for positional tracking, and applying various image processing algorithms.
2:Sample Selection and Data Sources:
The experiments used MOI images from inspections of aircraft components, such as rivet holes with cracks, to evaluate the enhancements.
3:List of Experimental Equipment and Materials:
Equipment included a commercial MOI 308/307 system, rotary quadrature encoder, video capture card, laptop computer, and custom imaging head fixture. Materials involved garnet films and other components of the MOI system.
4:Experimental Procedures and Operational Workflow:
The procedure involved mounting the encoder on the MOI scan head, digitizing video signals to save images at fixed intervals based on encoder rotation, stitching images into a single composite, and applying image processing techniques (e.g., background subtraction, contrast adjustment) in real-time or post-inspection.
5:Data Analysis Methods:
Data analysis utilized image processing techniques implemented in MATLAB, including histogram adjustment, background subtraction, de-noising, contrast stretching, morphological operations, and thresholding to enhance image clarity and detect flaws.
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