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Quality Control

Quality Control

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Quality Control

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  1. Quality Control Chapter 6- Additional SPC Techniques for Variables PowerPoint presentation to accompany Besterfield Quality Control, 8e PowerPoints created by Rosida Coowar

  2. Outline Continuous and Batch Processes Multi-Vari Chart Short-Run SPC Gage Control

  3. Learning Objectives When you have completed this chapter you should be able to: Explain the difference between discrete, continuous, and batch processes. Construct and use a group chart. Construct a multi-vari chart. Calculate the central line and control limits of a specification chart

  4. Learning Objectives-Continued When you have completed this chapter you should be able to: Explain how to use precontrol for set up and run activities. Calculate the central line and central limits for a Zbar,W and Z,W charts Be able to perform G R&R

  5. Continuous Processes • Usually operates 24 hrs a day, 7 days a week, stops for scheduled maintenance. • Often involves a conveyor or moving assembly line • Associated with product involving hazardous materials

  6. Continuous Processes • Examples • Paper-making machines • Oil refineries • Soft drinks (continuous then discrete) • Control charts for each value (multiple stream output)

  7. Continuous Processes • It is extremely important to have knowledge about the process and objectives for the control chart. When it is difficult to obtain samples from a location, sensors may be helpful to collect data, compare to control limits, and automatically control the process.

  8. Continuous & Batch Processing A good example of a continuous process is depicted by the paper-making process. They operate 24 hrs a day, 7 days a week, and stop only for scheduled maintenance or emergencies. Observed values are taken in the machine direction (md) or cross-machine direction (cd).

  9. Continuous & Batch Processing If the flow of pulp is controlled by 48 valves, then 48 md control charts would be required to control each valve. In this case it is very important for the practitioner to be knowledgeable about the process and have definite objectives for the control chart.

  10. Group Chart • Eliminates the need for a chart for each stream; however, it does not eliminate the need for measurements at each stream • Uses the same methodology outlined in Chapter 5, 25 subgroups for each stream. • Use lowest and highest averages for Xbar chart and the highest range for the R chart. Each stream has a number.

  11. Group Chart • Any out-of-control situation would call for corrective actions • We have the out-of-control situation when the same value streams gives the highest or lowest value r times in succession

  12. Group Chart • See Table 6-1 for the r values • The technique is applicable to machines, test equipment, operators, or suppliers as long as: • Each stream has the same target • Same variation • Variation is as close to normal as required by conventional Xbar and R charts.

  13. Batch Processes • Paint, soft drinks, bread, soup, etc • SPC of batches has two forms: • Within batch variation • Only one observed value of a particular quality characteristic can be obtained • They need to be obtained at different locations within the batch • Between-batch variation • Does not always occur

  14. Batch Chart • It is not a control chart, it might be more appropriately called a run chart • Can provide information for effective quality improvement

  15. Batch Chart Figure 6-4 Batch chart for different batches with different specifications

  16. Multi-Vari Chart • For detecting different types of variation that are found in products and services • The chart will lead to a problem solution much faster than other techniques • Shows the different types of variation within a single unit or service (within unit variation, unit-to-unit variation, time-to-time variation)

  17. Multi-Vari Chart • Procedure: • Select 3 to 5 consecutive units • Plot the highest and lowest observed value of each piece • Draw a line between them • Repeat the process

  18. Short-Run SPC Specification Chart • Used for small lot sizes – common in JIT • Gives some measure of control and a method of quality improvement • Central line and control limits are established using the specification σ = (USL-LSL) / 6 Cp

  19. Specification Chart These limits represent what we would like the process to do.

  20. Deviation Chart Deviation Chart • The plotted points are the deviation from the target • Even though the target changes, the central line for the X chart is always zero • Because the target changes, we require the variance (S2) of the different targets or nominal to be identical. This requirement is verified by ANOVA or by using

  21. Deviation Chart Deviation Chart • The plotted points are the deviation from the target • Even though the target changes, the central line for the X chart is always zero • Because the target changes, we require the variance (S2) of the different targets or nominal to be identical. This requirement is verified by ANOVA or by using

  22. Deviation Chart Deviation Chart • Advantages: • Provide the opportunity to convey enhanced information • Can plot different quality characteristics on the samechart

  23. Deviation Chart Figure 6-8 Deviation chart for individuals (X’s) and moving range (R’s)

  24. ZBar and W Charts Those are very good for short runs

  25. ZBar and W Charts Figure 6-10 ZBar chart

  26. Short-Run SPC See Chapter 6 page 256 for more details

  27. ZBar and W Charts

  28. ZBar and W Charts • Very good for short runs • Different quality characteristics such as length, width etc. may be plotted on the same chart • Can be used to monitor an operator’s daily performance • Can track an entire part history • Subgroup size MUST remain constant • Calculations are more involved

  29. Z and MW Charts MW chart uses the absolute value

  30. Precontrol Precontrol is an algorithm for controlling a process using its tolerances. • It does not monitor the stability of the process over time. • Unstable process may be within specifications today, but no guarantee that this will be true tomorrow.

  31. Precontrol • Specifications may change • Difficult to learn which factors affect the process if the process is not repeatable • Any deviation from the target leads to a loss

  32. Precontrol • Was developed originally with machining operations in mind. • Operator faced with the problem of first setting up the machine and then deciding if the machine is ready for full production. • Small lot sizes with each piece taking a long time to produce.

  33. Precontrol • Well suited for machining operations where one can devise simple feedback algorithms to bring the process back on target. • Requires operators who are very knowledgeable about the process.

  34. Precontrol Chart • “Stoplight” control • Prevention of defects even at start-up • Statistical charting method of control • 3 sigma processes • Product specification (tolerance) limits • Zones • Warning (yellow) • Stop process (red) • Statistically robust • Adopted by many companies since 1950s

  35. Precontrol • Steps for the construction: • Be sure that the process capability is less than the specifications. • PC lines are established to divide the tolerance into five zones (Figure 6-12) • The PC procedure has two stages: • Start-up • Run

  36. Precontrol • Steps for the construction: • Be sure that the process capability is less than the specifications. • PC lines are established to divide the tolerance into five zones (Figure 6-12) • The PC procedure has two stages: • Start-up • Run

  37. Precontrol • Steps for the construction: • Be sure that the process capability is less than the specifications. • PC lines are established to divide the tolerance into five zones (Figure 6-12) • The PC procedure has two stages: • Start-up • Run

  38. Precontrol • Steps for the construction: • Be sure that the process capability is less than the specifications. • PC lines are established to divide the tolerance into five zones (Figure 6-12) • The PC procedure has two stages: • Start-up • Run

  39. Precontrol • Assumes • Normal Distribution • USL – LSL = 6 Sigma • Any shift in mean -> increase in defects • False positives: • 2% chance of two consecutive products in same yellow zone • Nearly 0% false positives for any product in red zone

  40. Precontrol • PRE-Control Rules: • Qualifying Process • 5 consecutive units in green zone –o.k. to run • 1 yellow, restart counting • 2 yellow in a row, adjust the process • 1 red, adjust the process • < 5 signifies: • Process capability << 1 • PRE-control is not appropriate

  41. Precontrol Precontrol Rules cont’d: • Sampling • n = 2 units • Sample six pairs between adjustments. See Table 6.2 stoppages • 1 unit in red zone • 2 consecutive units in opposite yellow zone • 2 consecutive yellow zones , process adjusted and procedure goes back to start up

  42. Precontrol • Precontrol can be used for single specifications • Precontrol can be used for attributes • Precontrol is also used for visual characteristics by assigning visual standards for the Precontrol lines

  43. % Tolerance Precontrol Chart • Z Charts: Ability to accommodate more than one quality characteristic • We can combine Z chart into one technique by the use of percent tolerance precontrol chart (PTPCC) • Target or nominal • X*= (X – nominal) / [(USL-LSL)/2] • A negative value indicates that the observed value is below the nominal

  44. Gage Repeatability and Reproducibility Gage Repeatability and Reproducibility (GR&R) studies provide information on measurement system performance by analyzing measurement error from various sources. Typically the sources of variation are divided into three categories: part-to-part, operator or appraiser, and gage or equipment. In some instances another category, interaction between parts and operators, can provide additional information about the gaging process.

  45. Gage Repeatability and Reproducibility Not all calculation methods provide the same information or the same result, so it is important to pick an appropriate calculation method.

  46. Gage Control • All data have measurement errors • An observed value has two components Observed value = True value + Measurement error • Total Variation = Production Variation (process) + Measurement Variation

  47. Gage Control Measurement Variation = Repeatability (equipment variation) + Reproducibility (inspector or appraiser variation)

  48. Gage R&R METHODS: In most GR&R studies, multiple operators measure the same parts in random order to collect data. Most studies use two or three operators and five to 10 parts. In instances such as automated measuring processes, the GR&R studies are not affected by operator influence. These data then are analyzed using a calculation method.

  49. Gage R&R • Gage has to be calibrated using standards • Data is collected • 2 or 3 appraisers • 2 or 3 trials • 10 parts • Part characteristic is measured in a random order

  50. Gage R&R • Calculations • Average and range for each part is calculated by an appraiser • Values are averaged to obtain: • Data is collected • 2 or 3 appraisers • 2 or 3 trials • 10 parts • Part characteristic is measured in a random order