다구찌방법을 이용한 커버유리의 이산화탄소 레이저 드릴링 가공 (CO2 Laser Drilling Processing of the Cover Glass using the Taguchi Method)

서지정보

· 발행기관 : 대한용접접합학회
· 수록지 정보 : 대한용접접합학회지 / 37권 / 3호 / 199 ~ 205페이지
· 저자명 : 신중한, 최동혁

초록

This study reports CO2 laser drilling of the cover glass using the Taguchi method. In this study, the Taguchi method containing L16 (4 factors and 4 levels) orthogonal array design was employed to investigate the effect of process parameters on hole quality. Based on the results of experiments, holes with a high total hole quality index (THQI) show less cracking and heat affected zone (HAZ), and in this case the circular morphology of a hole is relatively well kept. From the Taguchi analysis, it is found that the THQI is significantly affected by laser power, however the effect of the focal position on the THQI is very small. The optimal process parameters to maximize the THQI are the laser power of 20 W, the scan speed of 20 mm/s, the scan of 1 pass and the focal position of 0 mm. The regression model to predict the THQI is obtained using the surface response method. It is confirmed that the regression model predicts the THQI with 5.717 % error (average).

영어초록

This study reports CO2 laser drilling of the cover glass using the Taguchi method. In this study, the Taguchi method containing L16 (4 factors and 4 levels) orthogonal array design was employed to investigate the effect of process parameters on hole quality. Based on the results of experiments, holes with a high total hole quality index (THQI) show less cracking and heat affected zone (HAZ), and in this case the circular morphology of a hole is relatively well kept. From the Taguchi analysis, it is found that the THQI is significantly affected by laser power, however the effect of the focal position on the THQI is very small. The optimal process parameters to maximize the THQI are the laser power of 20 W, the scan speed of 20 mm/s, the scan of 1 pass and the focal position of 0 mm. The regression model to predict the THQI is obtained using the surface response method. It is confirmed that the regression model predicts the THQI with 5.717 % error (average).

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