A Machine Vision Based Monitoring System for the LCD Panel Cutting Wheel Degradation

Abstract In Liquid Crystal Display (LCD) panel cutting, in-line monitoring of tool wear is important to maintain the dimension precision of finished products and to avoid possible damages to the workpiece. However, limited by the space for camera installation and the miniature structure of the tool itself, monitoring the wear of LCD panel cutting wheel is not a simple task. In this research, we proposed a machine-vision based instrumentation system and a systematic methodology for cutting wheel degradation monitoring. The proposed method describes the degradation of cutting wheel by estimating the tooth height of the cutting wheel blade based on the partially observed random samples. A series of methods are proposed to improve the reliability of the results. The effectiveness of the proposed method is validated based on the field data that is collected from three maintenance cycles. The validation results demonstrate consistent degradation trend of the cutting wheel over production cycles.

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