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nonconforming units are manufactured The control chart is an online process-monitoring technique widely used for this purpose Recall the following from 1 Figure 1-10 illustrates that adjustments to common causes of variation increase the variation of a process whereas Fig 1-11 illustrates that actions should be taken in response to assignable causes of variation Control charts may also be used to estimate the parameters of a production process and, through this information, to determine the capability of a process to meet speci cations The control chart can also provide information that is useful in improving the process Finally, remember that the eventual goal of statistical process control is the elimination of variability in the process Although it may not be possible to eliminate variability completely, the control chart helps reduce it as much as possible A typical control chart is shown in Fig.





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16-1, which is a graphical display of a quality characteristic that has been measured or computed from a sample versus the sample number or time Often, the samples are selected at periodic intervals such as every hour The chart contains a center line (CL) that represents the average value of the quality characteristic corresponding to the in-control state (That is, only chance causes are present) Two other horizontal lines, called the upper control limit (UCL) and the lower control limit (LCL), are also shown on the chart These control limits are chosen so that if the process is in control, nearly all of the sample points will fall between them In general, as long as the points plot within the control limits, the process is assumed to be in control, and no action is necessary.

+ 1/2] n1r/ L) sin (fiIvt) sin (fiI [x + ~]) .

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However, a point that plots outside of the control limits is interpreted as evidence that the process is out of control, and investigation and corrective action are required to nd and eliminate the assignable cause or causes responsible for this behavior The sample points on the control chart are usually connected with straight-line segments so that it is easier to visualize how the sequence of points has evolved over time Even if all the points plot inside the control limits, if they behave in a systematic or nonrandom manner, this is an indication that the process is out of control For example, if 18 of the last 20 points plotted above the center line but below the upper control limit and only two of these points plotted below the center line but above the lower control limit, we would be very suspicious that something was wrong.

You should note that as -L/2 < Xo < L/2, the sine in the denominator of (2.91) never vanishes. Also notice that even though the general solution (2.85) is not necessarily separable, (2.91) is [i.e., (2.91) separates into a spatially dependent factor sin([1r / L]fi[x + L/2]} and a time-dependent factor sin([1r / L]fivt}]. The spatially dependent factor is what we call the mode. So our one-dimensional resonator produces the spatial field pattern (i.e., mode) sin([1r/L]fi[x + L/2]} when its fith resonance Wii = (1r / L }fiv is excited.

If the process is in control, all the plotted points should have an essentially random pattern Methods designed to nd sequences or nonrandom patterns can be applied to control charts as an aid in detecting out-of-control conditions A particular nonrandom pattern usually appears on a control chart for a reason, and if that reason can be found and eliminated, process performance can be improved There is a close connection between control charts and hypothesis testing Essentially, the control chart is a test of the hypothesis that the process is in a state of statistical control A point plotting within the control limits is equivalent to failing to reject the hypothesis of statistical control, and a point plotting outside the control limits is equivalent to rejecting the hypothesis of statistical control..

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