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    發佈:2022/07/01 16:02:23

    摘 要
    深度學習以卷積神經網路(convolutional neural network, CNN)為主要架構,針對資料集使用
    中,除了ResNet-34之外,AlexNet、VGG-16、GoogLeNet v1在辨識上均可準確地分類出有
    In this study, the automated optical inspection (AOI) in coordination with deep learning was
    employed to detect the defects such as burrs or residues of ceramic shell mold on the surface of
    in sandblasted investment casting parts. The objective is to reduce the errors caused by long
    hours of manual inspection and to prevent personnel from the work environments with loud noise
    and poor air quality lasted long periods of time. In this study the deep learning framework wasmainly based on convolutional neural networks (CNNs). Four classic CNN models, namely,
    AlexNet, VGG-16, GoogLeNet, and ResNet-34, were applied to the datasets for training,
    predicting, and classifying whether there are defects and their results were compared. The results
    revealed that in terms of the classification prediction, AlexNet, VGG-16, and GoogLeNet v1
    could accurately determine the defects, whereas ResNet-34 could not. AlexNet was the most
    accurate in detecting detects on the investment casting parts in this study; it presented a
    prediction accuracy of 99.53% for good products and 100.00% for the defective ones. A
    graphical user interface (GUI) including machine vision (MV), CNNs, and object tracking was
    also designed and can assist users for detecting defects such as burrs surfaces or residues of shell
    mold contained on the surfaces of sandblasted investment casting parts.
    Keywords: Automated optical inspection, investment castings, sandblasting defect detection,
    convolutional neural network, deep learning

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