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Prediction of surface roughness in different machining methods using a texture mask feature extraction method.

Hsu-Chia Pan, Jui-Wen Pan, Kao-Der Chang

    Applied Optics
    |January 6, 2023
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    Summary
    This summary is machine-generated.

    A new texture mask (TM) machine learning method accurately predicts metal surface roughness, even with image angle deviations. This efficient TM approach reduces training time compared to convolutional neural networks (CNNs).

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    Area of Science:

    • Materials Science and Engineering
    • Manufacturing Technology
    • Machine Learning Applications

    Background:

    • Accurate prediction of metal surface roughness is crucial for manufacturing quality control.
    • Traditional methods struggle with variations in image acquisition, such as angle deviation and illumination.
    • Existing machine learning models may require extensive training times.

    Purpose of the Study:

    • To propose and evaluate a novel texture mask (TM) machine learning method for metal surface roughness prediction.
    • To assess the TM method's robustness against angle deviation during image acquisition.
    • To compare the TM method's efficiency and accuracy against other feature extraction techniques and convolutional neural networks (CNNs).

    Main Methods:

    • Development of a texture mask (TM) machine learning algorithm for surface roughness analysis.
    • Experimental evaluation using metal surfaces produced by flat lapping and grinding (roughness average (Ra) < 1 um).
    • Comparative analysis of TM against other feature extraction methods and CNNs under varying irradiation and angle deviation conditions.

    Main Results:

    • The TM method demonstrated superior accuracy in predicting surface roughness, particularly when dealing with angle deviation issues.
    • TM achieved high accuracy (exceeding 91%), comparable to CNNs.
    • The TM method exhibited significantly reduced training time compared to the CNN approach.

    Conclusions:

    • The proposed texture mask (TM) method is an accurate and efficient technique for extracting surface texture information.
    • TM effectively mitigates problems associated with angle deviation in image acquisition.
    • The TM method is suitable for implementation in automated systems for metal surface roughness inspection.