ScholarWorks@성균관대학교

초록

Ultra Thin Glass (UTG) is a key component of foldable electronics due to its excellent flexibility and strength. However, water droplet remaining between UTG and vacuum stage after strength process disturbs vision systems. Such systems often perceive droplets as cracks, resulting in lower production efficiency. Due to the limitations of the system and budget, solutions with deep learning are challenging. The work presents a non-deep learning solution to adjust the surface roughness of vacuum stages. This improves defect classification between water droplet and real defect. A test environment was built using a 6axis robot, an air knife, and two ccd vision sets. Two stages (Ra ≈ 3.5 and Ra ≈ 5.0) were compared and tested. The rough stages had a higher water removal rate, and the over-detection rate was reduced to less than 1%. Studies show that even basic surface adjustments can increase inspection precision without complex AI. This provides an affordable and practical solution in the current production environment.

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