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유지선(Ji-Sun You),이득중(Duek-Jung Lee),오창석(Chang-Suk Oh),장중순(Joong Soon Jang) 한국신뢰성학회 2017 신뢰성응용연구 Vol.17 No.1
Purpose: The purpose of this study is to estimate the life time of OLED TV panel through electric current ADT(Accelerated Degradation Test). Methods: We performed accelerated degradation test for OLED TV Panel at the room temperature to avoid high temperature impact on the luminance. Results: we got more accurately the life time of the OLED TV when we applied ADT without temperature factor than including both current and temperature. Conclusion: Until now, the ADT of the OLED TV has been conducted with temperature and current at the same time for reducing test time and costs. We estimate incorrect life time when the temperature is adopted as an accelerated factor. Due to the high temperature impact on the luminance of the OLED TV panel. So as to solve this problem, we discard temperature and use electric current only.
주성분 분석을 이용한 고객 공정의 불량률 예측 모형 개발
장윤희(Youn-Hee Jang),손지욱(Ji-Uk Son),이동혁(Dong-Hyuk Lee),오창석(Chang-Suk Oh),이득중(Duek-Jung Lee),장중순(Joongsoon Jang) 한국신뢰성학회 2016 신뢰성응용연구 Vol.16 No.2
Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client’s manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client"s manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.