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Using the process estimates, what is the fallout level if the coded speci cations are 10 5 mm Estimate PCR and interpret this ratio 16-17 A normally distributed process uses 85% of the speci cation band It is centered at the nominal dimension, located halfway between the upper and lower speci cation limits (a) Estimate PCR and PCRk Interpret these ratios (b) What fallout level (fraction defective) is produced 16-18 Reconsider Exercise 16-5 Suppose that the quality characteristic is normally distributed with speci cation at 220 40 What is the fallout level Estimate PCR and PCRk and interpret these ratios 16-19 Reconsider Exercise 16-6 Suppose that the variable is normally distributed with speci cations at 220 50 What is the proportion out of speci cations Estimate and interpret PCR and PCRk 16-20 Reconsider Exercise 16-4(a).

Assuming that both charts exhibit statistical control and that the process speci cations are at 20 5, estimate PCR and PCRk and interpret these ratios 16-21 Reconsider Exercise 16-8 Given that the speci cations are at 60 10, estimate PCR and PCRk and interpret these ratios 16-22 Reconsider 16-7(b) What are the natural tolerance limits of this process 16-23 Reconsider 16-12 The viscosity speci cations are at 500 25 Calculate estimates of the process capability ratios PCR and PCRk for this process and provide an interpretation..

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Now you ll put together a complex effect in Pixel Bender. Within a single lter kernel, you can combine a bunch of different operations so that all the parts of the compound effect render at the same time. I ve put together a bad reception Pixel Bender kernel that combines vertical roll, sinusoidal distortion, noise distortion, color channel splitting, and some horizontal lines. This example uses some workarounds that might be helpful to any Pixel Bender developer. Because Pixel Bender doesn t have noise and you can t loop to generate any you use bitmap data and feed in the noise as an extra input. In general, when Pixel Bender can t generate the kind of input you need, try sending it in as image data.

16-8 16-8.1

Also, you pass in the dimensions of the image as parameter dimensions, because Pixel Bender can t actually tell you the size of images.

ATTRIBUTE CONTROL CHARTS P Chart (Control Chart for Proportions)

<languageVersion : 1.0;> kernel BadReception < namespace : "com.actionscriptbible"; vendor : "ActionScript 3.0 Bible"; version : 1; description : "Bad reception on your TV"; > { input image4 srcImage; input image4 noiseImage; output pixel4 dst; parameter float2 dimensions <minValue: float2(0.0, 0.0); defaultValue: float2(640.0, 480.0);>; parameter float vRoll <minValue: -200.0; defaultValue: 0.0; maxValue: 200.0;>; parameter float channelSplit <minValue: 0.0; maxValue: 50.0;>; parameter float noisyHDisplace <minValue: -100.0; maxValue: 100.0;>; parameter float sinHDisplace <minValue: -100.0; maxValue: 100.0;>; parameter float3 sinHDisplaceFactors <minValue: float3(0.0); maxValue: float3(10.0);>; parameter float3 sinHDisplaceOffsets <minValue: float3(-6.0); maxValue: float3(6.0);>; parameter float noiseLayer <minValue: 0.0; maxValue: 1.0;>; parameter float blackoutThresh <minValue: 0.0; maxValue: 1.0;>; void evaluatePixel() { float2 coord = outCoord(); //Vertical roll coord.y = mod(coord.y - vRoll, dimensions.y); //Displacements float2 displace = float2(0.0); displace.x = noisyHDisplace * (length(sampleNearest(noiseImage, float2(0.0, outCoord().y))) - 0.5); float3 sinDisplaces = sinHDisplace * sin( float3(coord.y / 100.0) * pow(sinHDisplaceFactors, float3(1.0, 2.0, 3.0)) + sinHDisplaceOffsets ); displace.x += sinDisplaces[0] + sinDisplaces[1] + sinDisplaces[2]; coord += displace; //Color channel splitting if (channelSplit == 0.0) { dst = sampleNearest(srcImage, coord); } else {

Often it is desirable to classify a product as either defective or nondefective on the basis of comparison with a standard. This classi cation is usually done to achieve economy and simplicity in the inspection operation. For example, the diameter of a ball bearing may be checked by determining whether it will pass through a gauge consisting of circular holes cut in a template. This kind of measurement would be much simpler than directly measuring the diameter with a device such as a micrometer. Control charts for attributes are used in these situations. Attribute control charts often require a considerably larger sample size than do their variable measurements counterparts. In this section, we will discuss the fraction-defective control chart, or P chart. Sometimes the P chart is called the control chart for fraction nonconforming. Suppose D is the number of defective units in a random sample of size n. We assume that D is a binomial random variable with unknown parameter p. The fraction defective

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