• 한국대기환경학회지
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• 제20권6호
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• pp.783-792
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• 2004

In this study, a thick-film semiconductor odor gas sensor for the detection of $CH_3$SH was developed using SnO$_2$ as the main substrate and was investigated in terms of its sensitivity and reaction time. In the process of manufacturing the sensor, Taguchi's design of experiment (DOE) was applied to analyze the effects of a variety of parameters, including the substrate, the additives and the fabrication conditions, systematically and effectively. Eight trials of experiments could be possible using the 27 orthogonal array for the seven factors and two levels of condition, which originally demands 128 trials of experiments without DOE. The additives of Sb$_2$O$_{5}$ and PdCl$_2$ with the H$_2$PtCl$_{6}$ ㆍ6$H_2O$ catalyst were appeared to be important factors to improve the sensitivity, and CuO, TiO$_2$, V$_2$O$_{5}$ and PdO were less important. In addition, TiO$_2$, V$_2$O$_{5}$ and PdO would improve the reaction time of a sensor, and CuO, Sb$_2$O$_{5}$, PdCl$_2$ and H$_2$PtCl$_{6}$ㆍ6$H_2O$ were negligible. Being evaluated simultaneously in terms of both sensitivity and reaction time, the sensor showed the higher performance with the addition of TiO$_2$ and PdO, but the opposite results with the addition of CuO, V$_2$O$_{5}$, Sb$_2$O$_{5}$ and PdCl$_2$. The amount of additives were superior in the case of 1% than 4%. H$_2$PtCl$_{6}$ㆍ6$H_2O$ would play an important role for the increase of sensor performance as a catalyst.nce as a catalyst.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2007년도 춘계학술대회 논문집
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• pp.285-288
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• 2007

The use of commercial finite element analysis software to perform the entire process analysis and springback analysis has increased fast for last decade. Pamstamp2G is one of commercial software to be used widely in the world but it has still not been perfected in the springback prediction accuracy. We must select the combination of input parameters for the highest springback prediction accuracy in Pamstamp2G because springback prediction accuracy is sensitive to input parameters. Then we study the affect of input parameters to use member part for acquiring high springback prediction accuracy in Pamstamp2G. First, we choose important four parameters which are adaptive mesh level at drawing stage and cam flange stage, Gauss integration point number through the thickness and cam offset on basis of experiment. Second, we make a orthogonal array table L82[(7)] which is consist of 8 cases to be combined 4 input parameters, compare to tryout result and select main factors after analyzing affect factors of input parameters by Taguchi's method in 6 sigma. Third, we simulate after changing more detail the conditions of parameters to have big affect. At last, we find the best combination of input parameters for the highest springback prediction accuracy in Pamstamp2G. The results of the study provide the selection of input parameters to Pamstamp2G users who want to Increase the springback prediction accuracy.