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Introduction

Introduction . Variable - a single quality characteristic that can be measured on a numerical scale . When working with variables, we should monitor both the mean value of the characteristic and the variability associated with the characteristic . Control Charts for and R (1).

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Introduction

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  1. Introduction • Variable - a single quality characteristic that can be measured on a numerical scale. • When working with variables, we should monitor both the mean value of the characteristic and the variability associated with the characteristic. Control Charts for Variables

  2. Control Charts for Variables

  3. Control Charts for and R (1) Notation for variables control charts • n - size of the sample (sometimes called a subgroup) chosen at a point in time • m - number of samples selected • = average of the observations in the ith sample (where i = 1, 2, ..., m) • = grand average or “average of the averages (this value is used as the center line of the control chart) Control Charts for Variables

  4. Control Charts for and R(2) Notation and values • Ri = range of the values in the ith sample Ri = xmax - xmin • = average range for all m samples •  is the true process mean • is the true process standard deviation Control Charts for Variables

  5. Control Charts for and R(3) Statistical Basis of the Charts • Assume the quality characteristic of interest is normally distributed with mean , and standard deviation, . • If x1, x2, …, xn is a sample of size n, then he average of this sample is • is normally distributed with mean, , and standard deviation, Control Charts for Variables

  6. Control Charts for and R (4) Statistical Basis of the Charts • The probability is 1 -  that any sample mean will fall between • The above can be used as upper and lower control limits on a control chart for sample means, if the process parameters are known. Control Charts for Variables

  7. Control Charts for and R (5) Control Limits for the chart • A2 is found in Appendix VI for various values of n. Control Charts for Variables

  8. Control Charts for and R (6) Control Limits for the R chart • D3 and D4 are found in Appendix VI for various values of n. Control Charts for Variables

  9. Control Charts for and R (7) Estimating the ProcessStandard Deviation • The process standard deviation can be estimated using a function of the sample average range. • This is an unbiased estimator of  Relative range： W=R/σ E(W)=E(R/σ)=d2 Control Charts for Variables

  10. ^ σR=d3(R/d2) Control Charts for and R (8) R=Wσ Var(W)=d32 σR=d3σ Control Charts for Variables

  11. Control Charts for and R (9) Trial Control Limits • The control limits obtained from equations should be treated as trial control limits. • If this process is in control for the m samples collected, then the system wasin control in the past. • If all points plot inside the control limits and no systematic behavior is identified, then the process was in control in the past, and the trial control limits are suitable for controlling current or future production. Control Charts for Variables

  12. Control Charts for and R(10) Trial control limits and the out-of-control process • If points plot out of control, then the control limits must be revised. • Before revising, identify out of control points and look for assignable causes. • If assignable causes can be found, then discard the point(s) and recalculate the control limits. • If no assignable causes can be found then 1) either discard the point(s) as if an assignable cause had been found or 2)retain the point(s) considering the trial control limits as appropriate for current control. If future samples still indicate control, then the unexplained points can probably be safely dropped. Control Charts for Variables

  13. Control Charts for and R(11) • Before revising, identify out of control points and look for assignable causes. • When many of the initial samples plot out of control against the trial limits, (as few data will remain which we can recompute reliable control limits.) it is better to concentrate on the pattern formed by these points. Control Charts for Variables

  14. Control Charts for and R(12) • When setting up and R control charts, it is best to begin with the R chart. Because the control limits on the chart depend on the process variability, unless process variability is in control, these limits will not have much meaning. Control Charts for Variables

  15. Example 5-1 Control Charts for Variables

  16. Estimating Process Capability • The x-bar and R charts give information about the capability of the process relative to its specification limits. Control Charts for Variables

  17. The fraction of nonconforming of Example 5-1 • Assumes a stable process. • Assume the process is normally distributed, and x is normally distributed, the fraction nonconforming can be found by solving: P(x < LSL) + P(x > USL) Control Charts for Variables

  18. 製程能力(process capability) • 意義 • 對於穩定之製程所持有之特定成果，能夠合理達成之能力界限。 • 製程能力的表示法 • 製程準確度(Ca值) • 製程精密度(Cp值)製程能力比(process capability ratio，PCR) • 製程能力指數(Cpk值) Control Charts for Variables

  19. 製程平均值-規格中心值 Ca= 規格公差/2 = X-μ = T/2 製程準確度Ca(capability of accuracy) T：規格公差=規格上限-規格下限 Control Charts for Variables

  20. 製程準確度之評價方法 • A級 • 小於等於12.5%理想的狀態，故維持現狀。 • B級 • 大於12.5%，小於等於25%儘可能調整改進至A級。 • C級 • 大於25%，小於等於50%應立即檢討並加以改善 • D級 • 大於50%，小於100%採取緊急措施，並全面檢討，必要時應考停止生產。 Control Charts for Variables

  21. 雙邊規格： 規格公差 T = Cp= 6σ 6個估計標準差 單邊規格： = 規格上限-製程平均值 SU-X = Cp= 3σ 3個估計標準差 或 = 製程平均值-規格下限 X-SL = Cp= 3σ 3個估計標準差 製程精密度Cp(process capability ratio，PCR) Control Charts for Variables

  22. 製程精密度評價方式 • A+級 • 大於等於1.67可考慮放寛產品變異，尋求管理的簡單化或成本降低方法。 • A級 • 小於1.67，大於等於1.33理想的狀態，故維持現狀。 • B級 • 小於1.33，大於等於1.00確實進行製程管制，儘可能改善為A級。 • C級 • 小於1.00，大於等0.67產生不良品，產品須全數選別，並管理改善製程。 • D級 • 小於0.67採取緊急對策，進行品質改善，探求原因並重新檢討規格。 Control Charts for Variables

  23. { } X-SL SU-X Cpk=min = = ， 3σ 3σ 製程能力指數Cpk Cpk=(1-|Ca|)Cp 當Ca=0時Cpk=Cp Control Charts for Variables

  24. 製程能力指數評價方式 • A級 • 大於等於1.33製程能力足夠 • B級 • 小於1.33，大於等於1.00能力尚可應再努力 • C級 • 小於1.00應加以改善 Control Charts for Variables

  25. Process Capability Ratios (Cp)(assume the process is centered at the midpoint of the specification band) If Cp > 1, then a low number of nonconforming items will be produced. • If Cp = 1, (assume norm. dist) then we are producing about 0.27% nonconforming. • If Cp < 1, then a large number of nonconforming items are being produced. Control Charts for Variables

  26. Control Charts for Variables

  27. The percentage of the specification band **The Cp statistic assumes that the process mean is centered at the midpoint of the specification band – it measures potential capability. Control Charts for Variables

  28. Revision of Control Limits and Center Line • The effective use of control chart will require periodic revision of the control limits and center lines. • Every week, every month, or every 25, 50, or 100 samples. • When revising control limits, it is highly desirable to use at least 25 samples or subgroups in computing control limits. • If the R chart exhibits control, the center line of the Chart will be replaced with a target value. This can be helpful in shifting the process average to the desired value. Control Charts for Variables

  29. Phase II Operation of Charts • Use of control chart for monitoring future production, once a set of reliable limits are established, is called phase II of control chart usage • A run chart showing individuals observations in each sample, called a tolerance chart or tier diagram, may reveal patterns or unusual observations in the data Control Charts for Variables

  30. Control Charts for Variables

  31. Control Limits, Specification Limits, and Natural Tolerance Limits(1) • Control limits are functions of the natural variability of the process • Natural tolerance limits represent the natural variability of the process (usually set at 3-sigma from the mean) • Specification limits are determined by developers/designers. Control Charts for Variables

  32. Control Limits, Specification Limits, and Natural Tolerance Limits(2) • There is no mathematical relationship between control limits and specification limits. • Do not plot specification limits on the charts • Causes confusion between control and capability • If individual observations are plotted, then specification limits may be plotted on the chart. Control Charts for Variables

  33. Control Limits, Specification Limits, and Natural Tolerance Limits(3) Control Charts for Variables

  34. Rational Subgroups • X bar chart monitors the between sample variability (variability in the process over time). • Sample should be selected in such a way that maximizes the chances for shifts in the process average to occur between samples. • R chart monitors the within sample variability (the instantaneous process variability at a given time). • Samples should be selected so that variability within samples measures only chance or random causes. Control Charts for Variables

  35. Guidelines for the Design of the Control Chart(1) • Specify sample size, control limit width, and frequency of sampling • if the main purpose of the x-bar chart is to detect moderate to large process shifts, then small sample sizes are sufficient (n = 4, 5, or 6) • if the main purpose of the x-bar chart is to detect small process shifts, larger sample sizes are needed (as much as 15 to 25)…which is often impractical…alternative types of control charts are available for this situation…see Chapter 8 Control Charts for Variables

  36. Guidelines for the Design of the Control Chart(2) • If increasing the sample size is not an option, then sensitizing procedures (such as warning limits) can be used to detect small shifts…but this can result in increased false alarms. • R chart is insensitive to shifts in process standard deviation for small samples. The range method becomes less effective as the sample size increases, for large n, may want to use S or S2 chart. • The OC curve can be helpful in determining an appropriate sample size. Control Charts for Variables

  37. Guidelines for the Design of the Control Chart(3) Allocating Sampling Effort • Choose a larger sample size and sample less frequently? or, Choose a smaller sample size and sample more frequently? • The method to use will depend on the situation. In general, small frequent samples are more desirable. • For economic considerations, if the cost associated with producing defective items is high, smaller, more frequent samples are better than larger, less frequent ones. Control Charts for Variables

  38. Guidelines for the Design of the Control Chart(4) • If the rate of productionis high, then more frequent sampling is better. • If false alarms or type I errors are very expensive to investigate, then it may be best to use wider control limits than three-sigma. • If the process is such that out-of-control signals are quickly and easily investigated with a minimum of lost time and cost, then narrower control limits are appropriate. Control Charts for Variables

  39. Changing Sample Size on the and R Charts1 • In some situations, it may be of interest to know the effect of changing the sample size on the x-bar and R charts. Needed information: • Variable sample size. • A permanent change in the sample size because of cost or because the process has exhibited good stability and fewer resources are being allocated for process monitoring. Control Charts for Variables

  40. Changing Sample Size on the and R Charts2 • = average range for the old sample size • = average range for the new sample size • nold = old sample size • nnew = new sample size • d2(old) = factor d2 for the old sample size • d2(new) = factor d2 for the new sample size Control Charts for Variables

  41. Changing Sample Size on the and R Charts3 Control Limits A2、D3、D4 are selected for the new sample size Control Charts for Variables

  42. Example 5-2 Control Charts for Variables

  43. Charts Based on Standard Values • If the process mean and varianceare known or can be specified, then control limits can be developed using these values: • Constants are tabulated in Appendix VI Control Charts for Variables

  44. Interpretation of and R Charts • In interpreting patterns on the X bar chart, we must first determinewhether or not the R chart is in control. • Patterns of the plotted points will provide useful diagnostic information on the process, and this information can be used to make process modifications that reduce variability. • Cyclic Patterns • Mixture • Shift in process level • Trend • Stratification Control Charts for Variables

  45. Control Charts for Variables

  46. The Effects of Nonnormality • In general, the X barchart is insensitive (robust) to small departures from normality. • In most cases, samples of size four or five are sufficient to ensure reasonable robustness to the normality assumption. Control Charts for Variables

  47. The Effects of Nonnormality R1 • The R chart is more sensitive to nonnormality than the chart • For 3-sigma limits, the probability of committing a type I error is 0.00461on the R-chart. (Recall that for , the probability is only 0.0027). Control Charts for Variables

  48. The Operating Characteristic Function(1) • How well the and R charts can detect process shifts is described by operating characteristic (OC) curves. • Consider a process whose mean has shifted from an in-control value by k standard deviations. If the next sample after the shift plots in-control, then you will not detect the shift in the mean. The probability of this occurring is called the -risk. Control Charts for Variables

  49. The Operating Characteristic Function(2) • The probability of not detecting a shift in the process mean on the first sample is L= multiple of standard error in the control limits k = shift in process mean (#of standard deviations). Control Charts for Variables

  50. The Operating Characteristic Function(3) • The operating characteristic curves are plots of the value  against k for various sample sizes. Control Charts for Variables

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