CorrectSPC PROCESS CONTROL FOR PRECISION MACHINING

2. CorrectSPCPROCESS CONTROL FOR PRECISION MACHINING © 2010 Bob Doering All Rights Reserved

3. STATISTICAL PROCESS CONTROL Results Index > 1.67 1.33≤ Index ≤ 1.67 Index ≤ 1.33 Interpretation Meets acceptance criteria May be acceptable Does not meet acceptance criteria AIAG PPAP 4th Edition 2.2.11.3 Acceptance Criteria for Initial Study The organization shall use the following as acceptance criteria for evaluating initial process study results for processes that appear stable.

4. STATISTICAL PROCESS CONTROL AIAG PPAP 4th Edition 2.2.11.5 Processes with One-Sided Specifications or Non-Normal Distributions The organization shall determine with the authorized customer representative alternative acceptance criteria for processes with one sided specifications or non-normal distributions.

5. STATISTICAL PROCESS CONTROL AIAG PPAP 4th Edition 2.2.11.5 Processes with One-Sided Specifications or Non-Normal Distributions NOTE: The above mentioned acceptance criteria (2.2.11.3) assume normality and a two-sided specification (target in the center). When this is not true, using this analysis may result in unreliable information.

6. STATISTICAL PROCESS CONTROL different type of index or some method of transformation of the data. The focus should be on understanding reasons for the non-normality (e.g. is it stable over time?) and managing variation. AIAG PPAP 4th Edition 2.2.11.5 Processes with One-Sided Specifications or Non-Normal Distributions NOTE (cont.): These alternate acceptance criteria could require

7. CONTROL CHART BASICS NORMAL PROCESS: IN CONTROL WITH CHANCE VARIATION IN ORDER FOR A PROCESS TO BE NORMAL, IT SHOULD BE ABLE TO BE SET AT THE MEAN, AND WILL CONTINUE TO RANDOMLY VARY ABOUT THE MEAN WITHOUT ANY OPERATOR INTERVENTION!

8. CONTROL CHART BASICS EXAMPLES OF PROCESSES WITH NORMAL VARIATION CUTTING GRASS

9. CONTROL CHART BASICS EXAMPLES OF PROCESSES WITH NORMAL VARIATION AUTOMATED BAKERY

10. WHAT IS CONTROL? must remain stable over time must operate in a stable and consistent manner must be set at the proper level (centered) the natural process spread must not exceed the product’s specified tolerance (capable) A process in control is in the ideal state 100% conforming and predictable

11. WHAT ARE THE TYPES OF VARIATION CAUSES? COMMON CAUSE Inherent in the process Affects every part Examples: gravity, air pressure, tool wear SPECIAL CAUSE “Assignable” Does not affect every part Examples: tool breakage, start up, change of operators

12. TRADITIONAL SPC Expects all “special causes” have been eliminated Expects the remaining variation is random, with most variation close to a central value Seeks to find trends from within an otherwise random output to act upon when they occur

13. TRADITIONAL SPC Search for Extraterrestrial Intelligence (SETI) Traditional SPC is like looking out into space seeking signs of life

14. TRADITIONAL SPC Typically, you listen to random radio frequency noise looking for a “pattern” – as a pattern shows intent

15. TRADITIONAL SPC Wow! signal was a strong narrowband radio signal detected by Dr. Jerry R. Ehman on August 15, 1977, while working on a SETI project at The Big Ear radio telescope of The Ohio State University If you find such a trend, then you can take action to determine its origin. This is more “monitoring” than “control”

16. PROCESS DEFINITIONS DEFINITION OF PRECISION MACHINING A process where material is removed by a cutting surface – such as grinding, honing, turning, milling, etc. The process must be controlled in a manner that all variation (vibration, bearings, gage error) is statistically insignificant except tool wear.

17. CONTROL CHART DEFINITIONS TOTAL VARIANCE EQUATION s2T=s2Tool Wear+s2Measurement Error+ s2Gage Error+s2Material+s2Temperature +s2Operator +s2Other

18. PROCESS DISTRIBUTION

19. PROCESS DISTRIBUTION GRINDING EXAMPLE

20. PROCESS DISTRIBUTION

21. PROCESS DISTRIBUTION

22. PROCESS DISTRIBUTION

23. PROCESS DISTRIBUTION

24. PROCESS DISTRIBUTION “Process Control and Evaluation in the Presence of Systematic Assignable Cause”, Ashok Sarkar and Surajit,Pal, Quality Engineering, Volume 10(2), 1997-1998

25. PROCESS DISTRIBUTION

26. PROCESS DISTRIBUTION

27. CENTRAL LIMIT THEOREM sufficiently large number of independent random variables each with finite mean and variance, will be approximately normally distributed (Rice 1995). The central limit theorem (CLT)

28. CENTRAL LIMIT THEOREM of independent sufficiently large number of dependent random variables non-random variables each with finite mean and variance, will be not be normally distributed The central limit theorem does not apply! Precision machining has

29. CENTRAL LIMIT THEOREM

30. CENTRAL LIMIT THEOREM

31. CONTROL CHART FEATURES Interesting points: The MEAN has no real value in controlling a process with the uniform distribution “Running to the mean” is not how to control a process with the uniform distribution – it causes overcontrol!

32. TYPES OF VARIABLE CONTROL CHARTS There are many types, but the most common on the precision machining shop floor is: X Bar-R (or X Mean - R) X-Moving Range and then there is a new option: X Hi/Low – R But, which is best?

33. X-BAR R CHARTS

34. X-BAR R CHARTS

35. X-BAR R CHARTS Control Chart Data Collection Key Question For Machining Round Parts: How many diameters are there in a circle? d

36. X-BAR R CHARTS Control Chart Data Collection How many diameters are there in a circle? There are an infinite number of diameters in a circle!

37. CONTROL CHART DEFINITIONS

38. X-BAR R CHARTS

39. X-BAR R CHARTS Control Chart Data Collection There are also an infinite number of lengths in a linear feature!

40. X-BAR R CHARTS Why are X-bar – R chart control limits ridiculously tight for precision machining? • Because they are based on the range of your sample. • The variation of the range of your sample is nearly zero, except for your measurement error! • It has nothing to do with your process variation over time!

41. X-BAR R CHARTS • The X bar chart from the X bar – R charts represent the average of an insignificant sample of measurements for a of a circular feature • Measuring multiple samples is a waste of time in precision machining • R charts from the X bar – R charts represent the range of measurement error • Control limits are calculated using statistics for the wrong distribution – the normal distribution

42. PRECONTROL CHARTS

43. X-MR CHART

44. X-MR CHART • You mightbe able to use the X-MR chart if your roundness is insignificant versus your specification • If you use the X-MR chart, you cannot use the traditional calculations for control limits for the X chart – you must use the control limits for the uniform distribution • If you use the X-MR chart, you can use the traditional calculations for control limits for the MR chart

45. Rule No.1 Original data should be presented in a way that will preserve the evidence of the original data for all the predictions assumed to be useful. -Dr. Walter A. Shewhart Statistical Method from the Viewpoint of Quality Control

46. CONTROL CHART DATA Rule of life: If you measure one diameter, you will measure a good one …and the customer will measure a bad one!

47. CONTROL CHART DATA How do you control diameters? Measure the part and record the largest and smallest diameters – then you are controlling all possible diameters!

48. CONTROL CHART DATA

49. CONTROL CHART DATA What else can you learn from the largest and smallest diameter? The difference between the largest and smallest diameter – by definition – is the roundness!

50. CONTROL CHART DATA ROUNDNESS STORY