Abstract

Recently, there is an increasing interest in analysis of big data that is coming from manufacturing industry. In this paper, we use PCB (Printed Circuit Board) manufacturing data to provide manufacturers with information on areas with high PCB defect rates, and to visualize them to facilitate production and quality control. We use the K-means and DBSCAN clustering algorithms to derive the high fraction of PCB defects, and compare which of the two algorithms provides more accurate results. Finally, we develop a system of MVC structure to visualize the information about bad clusters obtained through clustering, and visualize the defected areas on actual PCB images.

Keywords

• Manufacturing Data;
• PCB;
• Defected Region

• 제조 데이터;
• 고장 영역;

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Acknowledgement

Supported by : 산업통상자원부, 정보통신기술진흥센터

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