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The center line and upper and lower control limits for the P chart are UCL p 3 p11 n p2 CL p LCL p 3 p11 n p2 (16-24)

Table 16-4 Number of Defectives in Samples of 100 Ceramic Substrates Sample 1 2 3 4 5 6 7 8 9 10 No. of Defectives 44 48 32 50 29 31 46 52 44 48 Sample 11 12 13 14 15 16 17 18 19 20 No. of Defectives 36 52 35 41 42 30 46 38 26 30





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Repeating this derivation for the flux :F dQ through an infinitesimal hypercube, we obtain another integral theorem, [146] (C.30) In the complex plane, a function I(z) is analytic in a domain D when





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These control limits are based on the normal approximation to the binomial distribution. When p is small, the normal approximation may not always be adequate. In such cases, we may use control limits obtained directly from a table of binomial probabilities. If p is small, the lower control limit obtained from the normal approximation may be a negative number. If this should occur, it is customary to consider zero as the lower control limit. EXAMPLE 16-4 Suppose we wish to construct a fraction-defective control chart for a ceramic substrate production line. We have 20 preliminary samples, each of size 100; the number of defectives in each sample is shown in Table 16-4. Assume that the samples are numbered in the sequence of production. Note that p (800 2000) 0.40; therefore, the trial parameters for the control chart are UCL LCL 0.40 0.40 3 3 10.40210.602 100 B 10.402 10.602 100 B 0.55 0.25 CL 0.40

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dz I(z) =

The control chart is shown in Fig. 16-16. All samples are in control. If they were not, we would search for assignable causes of variation and revise the limits accordingly. This chart can be used for controlling future production. Although this process exhibits statistical control, its defective rate ( p 0.40) is very poor. We should take appropriate steps to investigate the process to determine why such a large number of defective units is being produced. Defective units should be analyzed to determine the speci c types of defects present. Once the defect types are known, process changes should be investigated to determine their impact on defect levels. Designed experiments may be useful in this regard.

Computer software also produces an NP chart. This is just a control chart of nP D, the number of defectives in a sample. The points, center line, and control limits for this chart are just multiples (times n) of the corresponding elements of a P chart. The use of an NP chart avoids the fractions in a P chart.

(C.31)

0.6 0.5 0.4 0.3 0.2 0.1 p

16-8.2

for every closed contour C in D. Analogously, we define for quaternion functions the ideas of left regular and right regular (now the order is important). We call calF a left regular function in a domain D if for every closed hypersurface 8a in D,

U Chart (Control Chart for Defects per Unit)

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