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Author: search.filters.author.Krungseanmuang, WoranidthaSubject: search.filters.subject.defect detection

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    Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification
    (2024-01-01)
    Purahong, Boonchana
    ;
    Krungseanmuang, Woranidtha
    ;
    Tenghongsakul, Kasi
    ;
    Archevapanich, Tuanjai
    ;
    Khunthawiwone, Parkpoom
    This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.
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    Modern Manufacturing for Alloy Wheel Defect Detection using Image Processing and Application
    (2024-01-01)
    Archevapanich, Tuaniai
    ;
    Krungseanmuang, Woranidtha
    ;
    Chaowalittawin, Vasutorn
    ;
    Sathaporn, Posathip
    ;
    Chaowalittawin, Punyisa
    This paper presents an innovative approach to identifying defects in alloy wheel production by integrating image processing techniques with a mobile application platform. The system receives X-ray alloy images from the factory via mobile phone, processes them using image processing techniques to enhance clarity and readiness for defect detection, and then transmits the processed images to a Django framework via a uniform resource locator (URL). Subsequently, the system detects defects in the images, encodes them in Base64 format, and sends them to the mobile application through an API (Application Program Interface) for display on the user interface. This well-designed system architecture offers manufacturers a comprehensive solution to ensure product quality, reduce costs, and enhance customer satisfaction.