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Chapter 9

Chapter 9. Other Univariate Statistical Process Monitoring and Control Techniques. 9-1. Statistical Process Control for Short Production Runs . 9-1.1 and R Charts for Short Production Runs

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Chapter 9

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  1. Chapter 9 Other Univariate Statistical Process Monitoring and Control Techniques Introduction to Statistical Quality Control, 4th Edition

  2. 9-1. Statistical Process Control for Short Production Runs 9-1.1 and R Charts for Short Production Runs • Deviation from nominal (DNOM) control chart can be used for short production runs. The procedure is: • Calculate xi = Mi – Tk, the deviation from nominal for each observation. (Where Mi is the ith actual measurement and Tk is the target value for a particular group or part.) • Calculate and R for each sample using the deviations. • Construct standard and R charts for the deviations calculated. Introduction to Statistical Quality Control, 4th Edition

  3. 9-1. Statistical Process Control for Short Production Runs 9-1.1 and R Charts for Short Production Runs • Using data in Table 9-1, the control charts are Introduction to Statistical Quality Control, 4th Edition

  4. 9-1. Statistical Process Control for Short Production Runs 9-1.1 and R Charts for Short Production Runs Important points about relative to the DNOM approach: • An assumption is that the process standard deviation is approximately the same for all parts. If this assumption is invalid, use standardized and R charts. • This procedure works best when the sample size is constant for all part numbers • Deviation from nominal control charts have intuitive appeal when the nominal specification is the desired target value for the process. Introduction to Statistical Quality Control, 4th Edition

  5. 9-1. Statistical Process Control for Short Production Runs 9-1.1 Standardized and R Charts • If process standard deviations are different for different part numbers, the deviation from nominal control charts will not work effectively. • Standardized control charts can effectively handle the situation of different process standard deviations. Introduction to Statistical Quality Control, 4th Edition

  6. 9-1. Statistical Process Control for Short Production Runs 9-1.1 Standardized and R Charts • - average range for ith part • Ti – nominal value of x for the ith part • For all samples from this part number is plotted on a standardized R chart with LCL = D3 and UCL = D4 Introduction to Statistical Quality Control, 4th Edition

  7. 9-1. Statistical Process Control for Short Production Runs 9-1.1 Standardized and R Charts • - average range for ith part • Ti – nominal value of x for the ith part • For all samples from this part number is plotted on a standardized chart with LCL = -A2 and UCL = A2 Introduction to Statistical Quality Control, 4th Edition

  8. 9-1. Statistical Process Control for Short Production Runs 9-1.2 Attribute Control Charts for Short Production Runs • Standardized control charts for attributedata can be used for short production runs. • These charts are discussed in Chapter 6. • Table 9-2 provides a summary of the standardized attribute control charts for short production runs. Introduction to Statistical Quality Control, 4th Edition

  9. 9-1. Statistical Process Control for Short Production Runs 9-1.2Attribute Control Charts for Short Production Runs Introduction to Statistical Quality Control, 4th Edition

  10. 9-2. Modified and Acceptance Control Charts • When a high level of process capability has been achieved, it may be useful to relax the level of surveillance provided by standard control charts. • Modified control limits for charts or acceptance control charts can be employed for this situation. Introduction to Statistical Quality Control, 4th Edition

  11. 9-2. Modified and Acceptance Control Charts 9-2.1 Modified Control Limits for the Chart • Modified control limits commonly used in the case Cp or Cpk > 1. • The modified control chart is concerned only with detecting whether the true process mean  is located such that the process is producing a fraction nonconforming in excess of some specified value, . •  can vary over the range L    U Introduction to Statistical Quality Control, 4th Edition

  12. 9-2. Modified and Acceptance Control Charts 9-2.1 Modified Control Limits for the Chart • The upper and lower control limits for the modified control chart are Introduction to Statistical Quality Control, 4th Edition

  13. 9-2. Modified and Acceptance Control Charts 9-2.2 Acceptance Control Charts • Acceptance control charts take into account both the -risk and the -risk. • Two approaches to designing the acceptance control chart • Design the chart based on a specified n and process nonconforming  that we would like to reject with probability 1 - . • Choose a sample size for an acceptance control chart so that specified values of   , and  are obtained. Introduction to Statistical Quality Control, 4th Edition

  14. 9-2. Modified and Acceptance Control Charts 9-2.2 Acceptance Control Charts Approach 1: The control limits are Introduction to Statistical Quality Control, 4th Edition

  15. 9-2. Modified and Acceptance Control Charts 9-2.2 Acceptance Control Charts Approach 2: A sample size of will yield the required values of   , and . Introduction to Statistical Quality Control, 4th Edition

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