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Defect extraction method for additive manufactured parts with improved learning-based image super-resolution and the

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    This study introduces a new defect detection method for additive manufacturing (AM) using improved learning-based super-resolution (SR) and the Canny algorithm. The LSRC method significantly enhances image quality and defect extraction for better AM part quality control.

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    Area of Science:

    • Materials Science and Engineering
    • Manufacturing Technology
    • Image Processing

    Background:

    • Additive Manufacturing (AM) offers competitive advantages but faces limitations due to defects.
    • Effective defect detection is crucial for enhancing AM part quality.
    • Super-resolution (SR) technology can improve image quality for better defect analysis.

    Purpose of the Study:

    • To propose a novel defect extraction method for AM parts using enhanced learning-based image SR and the Canny algorithm (LSRC).
    • To evaluate the performance and robustness of the LSRC method compared to existing algorithms.
    • To demonstrate the significance of LSRC for defect information extraction in AM.

    Main Methods:

    • Developed a learning-based super-resolution (SR) method integrated with the Canny algorithm (LSRC) for defect extraction.
    • Employed direct mapping methodology for SR reconstruction.
    • Compared LSRC against bicubic interpolation and neighbor embedding (NE) algorithms.

    Main Results:

    • The LSRC method achieved superior performance in averaged information entropy (E), standard deviation (SD), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM).
    • LSRC demonstrated significant improvement rates over bicubic interpolation (0.45%–6.35%) and NE algorithm (0.97%–15.35%).
    • Experimental results confirm LSRC's enhanced reconstruction quality and robustness.

    Conclusions:

    • The proposed LSRC method significantly outperforms existing algorithms in SR reconstruction quality and robustness for AM defect detection.
    • LSRC is highly effective for extracting and analyzing critical defect information in additive manufactured parts.
    • This advancement contributes to improved quality control and reliability in AM processes.