1. VARIABLE CONTROL CHART : Mean and Dispersion  - Chart  R - Chart  S - Chart

2. 2 T a r g e t 1. To understand the Quality Characteristics 2. To understand the benefit of control chart 3. Able to develop the control chart 4. To know the control chart types 5. Able to evaluate the process using the control chart

3. 3 Introduction  The control chart can help to detect the change of process parameters.  Generally, there are two types of the control chart : 1. Variable control chart 2. Attribute control chart

4. 4 The Change of Process Parameter (Mean) LSLUSL 00 11

5. 5 The Change of Process Parameter (Standard Deviation) LSLUSL 00 00 11

6. 6 Quality Characteristics Variable Something that can be measured and expressed by the numerical scale. Attribute Something that can be classified into conforming or non conforming.

7. 7 Develop ing and the applicat ion of control charts To choose the quality characteristics Pareto analysis Implementation : Process evaluation using the control chart. Developing the control chart :  Preparation  Making the control chart

8. 8 To Choose the Quality Characteristic  Product has many the quality characteristics.  Choose the quality characteristics using the Pareto analysis.

9. 9 Pareto Analysis (1) Defect Code DefectFrequencyPercentage 1234567812345678 Outside diameter of hub Depth of keyway Hub length Inside diameter of hub Width of keyway Thickness of flange Depth of slot Hardness 30 20 60 90 30 40 50 20 8.82 5.88 17.65 26.47 8.82 11.77 14.71 5.88

10. 10 Pareto Analysis (2) Defect code Percentage of defects

11. 11 Preparation to use the control chart  Choose the sample  Sample size  Sampling Frequency  Choose the instrument for measurement  Design the form used to collect the data

12. 12 Making the Control Chart X-bar and Range chart X-bar and standard deviation chart Non target based Target based Non target based Target based

13. 13 X-bar and R Chart(1)  Step 1 Write the measurement of the quality characteristic in a Form.  Step 2 Calculate Mean and Range for each sample.

14. 14 X-bar and R Chart(2)  Step 3 Determine and draw a center line and trial control limits for every chart. X- bar chart Center Line Control Limit

15. 15

16. 16 X-bar and R Chart(3) R - Chart Center Line Control Limits

17. 17 X-bar and R Chart(4)  Step 4 Plotting the range value at R-Chart. Determine whether the point plotted in the statistical control. If not, identify the assignable causes that related to the out-of-control point and then perform the improvement to eliminate the assignable causes.

18. 18 X-bar and R Chart(5)  Step 5 Eliminate the out-of-control point after performing the improvement. Use the rest of sample to revise the center line and the control limits.  Step 6 Implement the control chart.

19. 19 Example 1 Consider a process by which coils are manufactured. Samples of size 5 are randomly selected from the process, and the resistance values (in ohms) of the coils are measured. The data values are given in Table 7-2, as are the sample mean X bar and the range R.

20. 20 Example 1 (continue) Table 7-2 SampleObservationsX barRComments 1 3. 22 23 25 20,22,21,23,22 25,18,20,17,22. 21,18,18,17,19 21,24,24,23,23 19,20,21,21,22 21.6 20.40. 18.6 23.00 20.6 38...43338...433 New vendor High Temp. Wrong die Sum521.0087

21. 21 Example 1 (continue) The initial of R-chart Center Line Trial Control Limits

22. 22 Example 1 (continue) R-chart Revision 1 Revised Center Line Revised Control Limit 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

23. 23 Example 1 (Continue) The initial of X-bar chart Center Line Trial Control Limits 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

24. 24 Example 1 (Continue) R-chart Revision 2 Revised Center Line Revised Control Limits 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 24 25

25. 25 Example 1 (Continue) X-bar chart for revision 1) Center Line Control Limits 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 24 25

26. 26 Standardized Control Chart (1)  It is used when the sample size is not the same.  The Statistic is standardized by subtraction the sample mean from the grand mean and divide it by the standard deviation.  The standard value represents the deviation from the mean with the unit of standard deviation.  The control limits for the standardized control chart is ± 3.

27. 27 Standardized Control Chart (2) Grand mean The estimation of Standard Deviation process The standardized value The Z i values are plotted in the control chart with CL=0, UCL=3 dan LCL=-3. The mean control chart :

28. 28 Standardized Control Chart (3) The value of r i The value of k i The k i values are plotted in the control chart with CL=0, UCL=3 dan LCL=-3. Range control chart

29. 29 R control chart The Control Limits base on Target X-bar control chart

30. 30 The Average and Standard Deviation Control Chart

31. 31 The Average and Standard Deviation Control Chart (No Standard) Standard Deviation chart Center Line Control Limit

32. 32 The Average and Standard Deviation Control Chart (No Standard) Average X-bar chart (grand mean) Center Line Control Limit

33. 33 The Average and Standard Deviation Control Chart (There is a Standard) Standard Deviation Chart Center Line Control Limit

34. 34 The Average and Standard Deviation Control Chart (There is a Standard) Average X-bar chart Center Line Control Limit

35. 35 Control Chart Pattern (Natural) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

36. 36 Control Chart Pattern (Sudden Shifts in the Level) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

37. 37 Control Chart Pattern (Sudden Shifts in the Level)  Change in proportions of materials coming from different sources.  New worker or machine.  Modification of production method or process.  Change in inspection device or method.

38. 38 Control Chart Pattern (Gradual Shifts in the Level) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

39. 39 Control Chart Pattern (Gradual Shifts in the Level)  The incoming quality of raw material or components changed over time.  The maintenance program changed.  The style of supervision changed.  New operator.  A decrease in worker skill due to fatigue.  A gradual improvement in the incoming quality of raw materials.

40. 40 Control Chart Pattern (Trending) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

41. 41 Control Chart Pattern (Trending)  Gradual deterioration of equipment.  Worker fatigue.  Deterioration of environmental conditions.  Improvement or deterioration of operator skill.  Gradual change in homogeneity of incoming material quality.

42. 42 Control Chart Pattern (Cyclic) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

43. 43 Control Chart Pattern (Cyclic)  Temperature or other recurring changes in physical environment.  Worker fatigue.  Differences in measuring or testing devices which are used in order.  Regular rotation of machines or operators.

44. 44 Control Chart Pattern (Freaks) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

45. 45 Control Chart Pattern (Freaks)  The use of a new tool for a brief test period.  The failure of a component.

46. 46 Control Chart Pattern (Bunches) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

47. 47 Control Chart Pattern (Bunches)  The use of a new vendor for a short period of time.  The use of different machine for a brief time period.  A new operator used for a short period.

48. 48 Control Chart Pattern (Mixture) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

49. 49  The differences in the incoming quality of material from two vendors.  Overcontrol.  Two or more machines being represented on the same control chart. Control Chart Pattern (Mixture)

50. 50 Control Chart Pattern (Stratification) 20 35 5 123456789101112131415 Sample Sample Average CL UCL LCL

51. 51 Control Chart Pattern (Stratification)  Incorrect calculation of control limits.  Incorrect subgrouping.

52. 52 Process Capability Capability Process Estimation is performed when the process is in control. Hitung standar deviasi proses. Proportion nonconforming item is performed by viewing the average, standard deviasi process, and specification limits (not the control limits).

53. 53 Example 2 The coil resistance specification is 21±3 ohms. The sample with size 5 is taken with the result R- bar equal to 3.50 and the process average estimation is 20.864. Determine the proportion of nonconforming output with assumption that the coil resistance data is normal distribution.

54. 54 Example 2 (continue) X USL=24LSL=18 0.02870.0188