5. Example
5.3 Application Example STN-LCD
Liquid crystals have been employed for display applications with various configurations. Most of the displays produced recently involve the use of either Twisted Nematic (TN) or Super Twisted Nematic (STN) liquid crystals, the technology of the STN display was introduced recently to improve the performance of LCD without using the TFT. A larger twist angle results in a significantly larger electro-optical distortion. This leads to a substantial improvement in the contract and viewing angles over TN displays. The STN-LCD products are popularly used in making the PDAs, notebook personal computers, word processors, and other peripherals. A typical assembly drawing for the STN-LCD product is depicted in Figure 14 and the custom glass and modules of the STN-LCD product is displayed in Figure 15.
Figure 12. An assembly drawing
for the STN-LCD product. Figure 2. The custom glass and modules of the STN-LCD product.
With an increasing number of personal computers are now network-ready and multimedia capable, equipped with CD-ROM drives. Due to advances in telecommunications technology, simple monochromatic displays are no longer in popular demand. The next generation of telecommunication products will require displays with rich, graphic quality images and personal interfaces. So future displays must be become clearer, sharper to meet these demands. Until this point, STN-LCD have been used mainly to display still images, and because of the slow response time needed to process still images, STN-LCD have not been able to reproduce animated images with an adequate contrast level. Thus, with the growing popularity of multimedia applications, there is a need for PCs equipped with color STN-LCD that are capable of processing animated pictures instead of only still images. The space between the glass substrate is filled with liquid crystal material, the thickness of the LC is kept uniform by using glass fibers or plastic balls as spacer, So the STN-LCD is sensitive in the thickness of the glass substrates.
To illustrate which has better process capability between the two suppliers, we present a case study on STN-LCD (Super Twisted Nematic Liquid Crystal Displays) manufacturing processes, which located on the Science-Based Industrial Park in Taiwan. These factories manufacture various types of the LCD. For a particular model of the STN-LCD investigated, the upper specification limit, USL of a glass substrate’s thickness is set to 0.77 mm, the lower specification limit, LSL of a glass substrate’s thickness is set to 0.63 mm, and the target value is set to T = 0.70 mm. If the characteristic data does not fall within the tolerance
(LSL USL, ), the lifetime or reliability of the STN-LCD will be discounted.
5.3.1 Data Analysis and Supplier Selection
For the Phase I of Supplier Selection problem, the practitioner should input the preset minimum requirement of Cpm values, and the minimal difference that must be differentiated between suppliers with designated selection power. If minimum requirement of STN-LCD product is Cpm=1.00, and δ =0.25 with selection power = 0.95. By checking Table 1 the sample size required for estimation is 204. Thus, the glass substrate’s thickness data taken from two LCD suppliers are displayed in Table 6. To confirm if the data of both suppliers normally distributed, we do the Shapiro-Wilk test for normality as shown in Figures 3-4. Because the p-values are larger than 0.05, we don’t reject the null hypothesis that the data are normally distributed. Histograms of both data for the two suppliers are displayed in Figures 5-6.
Quantiles of Standard Normal
data2
-3 -2 -1 0 1 2 3
0.660.680.700.720.740.76
Figure 14. Normal probability plot for thickness data of Supplier I.
Quantiles of Standard Normal
data1
-3 -2 -1 0 1 2 3
0.680.700.720.74
Figure 15. Normal probability plot for thickness data of Supplier II.
0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0
10 20 30 40 50
Figure 16. Histogram for supplier I.
0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0
10 20 30 40
Figure 17. Histogram for supplier II.
5.3.2 Phase I－Supplier Selection
To determine whether supplier II has better process capability than supplier I, that is, do the hypothesis testing comparing the two Cpm values, H C0: pm1≥Cpm2 versus H C1: pm1<Cpm2. First, we calculate the sample means, sample standard deviations, the sample estimators of Cˆpm, γˆ2, and νˆ for supplier I and supplier II, which summarized in Table 7.
Table 5. The calculated sample statistics for two suppliers.(Cpm) Population X S Cˆpm γˆ2 Rank γˆ2
I 0.7106 0.01695 1.1705 3.974×10-3 2 II 0.6998 0.01593 1.4687 2.524×10-3 1
Based on the selection procedure, the values w1 =1.241426 and
478218 .
2 =1
w . Choose the value of w which is larger than 1 and choose the value as small as possible, so w=min
{
w1,w2}
=1.241426. In this case one only need to compare the test statistic γˆi2, i=1,2, with the selection value w. Since2 1 2
2 ˆ
ˆ γ
γ ≤ w× and γˆ12 > w×γˆ22 then we conclude that the new supplier is better supplier with larger process capability Cpm.
5.3.3 Phase II－Magnitude Outperformed Detection
To further investigate the magnitude of the capability difference between the two suppliers, the supplier selection decisions would find a magnitude of q such that Cpm2 =Cpm1+q, where =q max{ q′ | test rejects Cpm1+q′≥Cpm2}. From the estimation of Phase I, we list the obtained selection values w and the decision based on the selection procedure for q = 0.01, 0.05, 0.10, 0.12(0.01)0.15 in Table 8.
Therefore, from the analysis of magnitude outperformed detection based on sample statistics, the magnitude of the difference between the two suppliers is q = 0.14. That is, we conclude that Cpm2 >Cpm1+0.14.
Table 6. Magnitude outperformed detection of selection procedure. (Cpm) ˆ 1
Cpm 1.1805 1.2205 1.2705 1.2905 1.3005 1.3105 1.3205 ˆ 2
Cpm 1.4687 1.4687 1.4687 1.4687 1.4687 1.4687 1.4687
q 0.01 0.05 0.10 0.12 0.13 0.14 0.15
w 1.241459 1.241602 1.241821 1.241922 1.241976 1.242032 1.242091 Decision Reject Ho Reject Ho Reject Ho Reject Ho Reject Ho Reject Ho Don’t reject Ho