Chapter 10 - Other Univariate Statistical...

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Other Univariate Statistical Process- Monitoring and Control Techniques Chapter 10 Montgomery, 6 th Edition
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Outline Statistical Process Control for Short Production Runs. Modified and Acceptance control Charts Control Charts for Multiple Stream Processes
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Outline Statistical Process Control for Short Production Runs. and R charts for Short Production Runs Attributes control Charts for Short Production Runs Other Methods Modified and Acceptance control Charts Control Charts for Multiple Stream Processes
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Statistical Process Control for Short Production Runs. and R Charts for Short Production Runs Attributes Control Charts for Short Production Runs Other Methods
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Introduction Most of the Short Production Control (SPC) methods are straightforward adaptations of the standard concepts and require no new methodology. One of the basic techniques of control charting used in the short-run environment is the use of the deviation from the nominal dimension as the variable on the control chart.
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and R Charts for SPR Simplest technique: Deviation from normal* (DNOM) control Chart This is instead of using the measured variable control chart. Short Production Runs * or deviation from nominal.
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Example
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When using deviation from nominal as the variable on the chart, it is not necessary to have a long production run for each part number. Recommendation: 20 samples before calculating the charts. Control limits were calculated using data from all 10 samples. Dashed vertical line to separate different products or parts.
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Important Highlights of DNOM An assumption is that the process standard deviation is approximately the same for all parts. I f this assumption is invalid, use a standardized and R chart (Later…) This procedure works best when the sample size is constant for all part numbers.
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Standardized and R charts If the process standard deviations are different for different part numbers, the deviation from nominal (or the deviation from process target) control charts will not work effectively. There is when the Standardized and R charts come to play. How?
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Let and be the average range and nominal value of for part j , respectively. Then, for all the samples i from this part number, plot On a standardize R chart with control limits at and . Standardized and R charts [cont’d]
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To plot the standardize chart, plot with control limits at and Note: The center line of the standardize chart is zero. Why? Standardized and R charts [cont’d]
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A: Because is the average of the original measurements for subgroups of the j th part number. The target values and for each part number can be determined by using specifications for and taking from prior history (often in the form of a control chart, or by converting an estimate of into by the relationship ).
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