This study attempted to overcome the limitations of the existing manual process by applying automation technology to the polishing process of hand tools (scaling tools) for removing tartar. The current polishing process is causing quality deviation, high defect rate, and safety problems depending on the skill level of the worker. Therefore, this study attempted to achieve precise control of the polishing angle and reduction of the defect rate at the same time by applying machine vision-based high-precision location recognition and deep learning correction algorithm. In particular, the performance was verified through experiments by manufacturing tray design and prototype automation equipment that can stably supply various tools. As a result of the experiment, the proposed system reduced the polishing angle error from ±2.5° to ±0.5° compared to the existing manual work, and the defect rate was reduced from 5% to less than 2%. In addition, the work efficiency improved by more than 30%. These achievements provide an important basis for reducing dependence on foreign equipment in the domestic dental hygiene educational equipment market and securing competitiveness in overseas markets.

• 이정익(인하공업전문대학교 기계공학과) | Jeong-Ick Lee Corresponding author