A Low-Cost IIoT-enabled Computer Vision-based System for Classifying Defect Types and Severity Levels in Industry 4.0
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Abstract
Industry 4.0 technologies such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) can assist in automating defect
detection and classification processes which are crucial for quality control in the manufacturing industry. However, there is still a barrier to adopting the technologies in Small and Medium Enterprises (SMEs) because of their limited budget. This
paper presents a low-cost defect detection and classifcation system and an interactive real-time dashboard monitoring IIoT data utilizing a singleboard computer and mainly open-source software. In the system, workpieces will be classified into non-defective (OK) and defective (NG) workpieces. Then, the NG workpieces will be further classified into defective types and severity levels. The workpiece used in the case study is a sticker on a 4.4 cm diameter bottle cap. The defect types are Off-Color, Missing Details, and Scratches, then each type is divided further into four severity levels. From evaluation, the system can achieve 96% when classifัying as
OK/NG and 88% accuracy in classifying defective types and levels. The system’s reliability is 100%. Based on experts’ opinions, the proposed system is relatively low-cost, reliable, and accurate for practical uses. The proposed system can be implemented locally or globally via a cloud server.
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