1. Chapter 36 Quality Engineering (Part 2) EIN 3390 Manufacturing Processes Fall, 2010

2. 36.3 Inspection to Control Quality
Inspection is the function that controls the quality manually, or automatically.
How much should be inspected:
Inspect every item being made
Sample
None. Assume that everything is acceptable or the product is inspected by customer, who will exchange it in case it is defective.

3. 36.3.1 Statistical Process Control (SPC)
Sampling requires statistical techniques for decisions about the acceptability of the whole based on sample’s quality. This is known as statistical process control (SPC).
The most widely used basic SPC techniques is the control charts.

4. Control charts for variables are used to monitor the output of a process by sampling, by measuring selected quality characteristics, by plotting the sample data on the chart, and then by making decisions about the performance of the process.

5. Figure 36 – 13 shows the basic structure of two charts commonly used for variable types of measurements.
The X chart tracks the aim (accuracy) of the process.
The R chart (or s chart) tracks the precision or variability of the process.
Usually, only X chart and R chart are used unless the sample size is large, and then s chart are used in place of R chart.

6. 36.3 Inspection to Control Quality

7. Quality Calculations
Sx/n
R = Xhigh – X low
FIGURE Quality control' chart calculations. On the charts, X plot and R values over time. The constants for calculating UCL and LCL values for the X and R charts are based on 3 standard deviations.
s = R/d2
A2 = 3/[d2 SQRT(n)]
Where, n – sample size

8. Samples are drawn over time.
Because some sample statistics tend to be normally distributed about their own mean, x value are normally distributed about x, and R values are normally distributed about R, and s values are normally distributed s.
Quality control charts are widely used as aids in maintaining quality and detecting trends in quality variation before defective parts are actually produced.

9. When sampling inspection is used, the typical sample sizes are from 3 to about 12.
Fig 16 – 4 shows one example of X and R charts for measuring a dimension of a gap on a part with 25 samples of size 5 over 6 days.

10. FIGURE 36-14 Example of X and R charts and the data set of 25 samples [k 25 of size 5 (n = 5)].
(Source : Continuing Process Critical and Process Capability Improvement, Statistical Methods Office, Ford Motor Co., 1985.)
0.178

11. Errors in Textbook

12. After control charts have been established, the charts act as a control indicator for the process.
If the process is operating under chance cause conditions, the data will appear random (no trends or pattern).
If X, R or s values fall outside the control limits or if nonrandom trends occur (like 7 points on one side of the central line or 6 successive increasing or decreasing points appear), an assignable cause or change may have occurred, and action should be taken to correct the problem.

13. 36.4 Process Capability Determination from Control Chart Data

14. 36.4 Process Capability Determination from Control Chart Data
After the process is determined to be “under control”, the data can be used to estimate the process capability parameters.
A sample size 5 was used in the example, so n = 5 (Fig 36 – 14). 25 groups of samples were drawn from the process, so K = 25.
For each sample, the sample mean x and sample range R are computed. For large samples, N > 12, the standard deviation of each sample should be computed rather than the range.

15. 36.5 Determining Causes For Problems in Quality
Fishbone diagram developed by Kaorw Ishikowa in 1943 is used in conjunction with control chart to root out the causes of problems.
Fishbone diagram is also known as Cause-and- effect.
Fishbone lines are drawn from the main line. These lines organize the main factors. Branching from each of these factors are even more detailed factors.

16. 36.5 Determining Causes For Problems in Quality
Four “M” are often used in fishbone diagram:
Men, Machines, Materials, and Methods.
CEDAC – Cause-and-effect diagram with the additional of cards. The effect is often tracked with a control chart. The possible causes of defects or problems are written on cards and inserted in slots in the cards.

17. Fishbone Diagram
FIGURE Example of a fishbone diagram using a control chart to show effects.

18. Fishbone Diagram
FIGURE Example of a fishbone diagram using a control chart to show effects.

19. 36.5.1 Sampling Errors Two kinds of decision errors:
Type I Error (a error): process is running perfectly, but sample data indicate that something is wrong.
Type II error (b error): process is not running perfectly and was making defective products, but sample data didn’t indicate that anything was wrong.

20. Errors FIGURE 36-16 When you look at some of the output from a
process and decide about the
whole (i.e., the quality of the
process), you can make two
kinds of errors.

21. Gage Capacity
Gage capability refers to inherent precision and accuracy of instruments.
Bias: poor accuracy or aim
Linearity: accuracy change over span of measurement
Stability: accuracy change over time
Repeatability: loss of precision in gage (variability)
Reproducibility: variation due different operators

22. Gage Capacity
Determining the capacity of the gage is called an R and R study.
10% Rule: Variation in a gage is about 10% less than the total tolerance spread (USL – LSL).

23. Total Observed Variation
FIGURE Gage
capability (variation) contributes
to the total observed variation in
the measurement of a part.

24. 36.5.3 Design of Experiments (DOE) and Taguchi Methods
SPC looks at processes and control, Taguchi methods loosely implies “improvement”.
DOE and Taguchi methods span a much wider scope of functions and include the design aspects of products and processes, areas that were seldom treated from quality standpoint view. Consumer is the focus on quality, and the methods of quality design and controls have been incorporated into all phases of production.

25. Taguchi Method FIGURE 36-18 The use of Taguchi methods can reduce the
inherent process variability, as
shown in the upper figure.
Factors A, B, C, and D versus
process variable V are shown in
the lower figure.

26. Six Sigma
FIGURE To move to six sigma capability from four sigma capability requires that the
process capability (variability) be greatly improved (s reduced). The curves in these figures represent
histograms or curves fitted to histograms.

27. 36.5.5 Total Quality Control (TQC)
Total Quality Control (TQC) was first used by A. V. Feigenbaum in may 1957.
TQC means that all departments of a company must participate in quality control (Table 36 – 1).

29. 36.5.6 Source, Self, and Successful Check and Poka-Yokes
Inspections can only find defects, not prevent them.
To achieve zero defects, a lean manufacturing concept must been developed where all operators are responsible for quality.
Source inspection involves rethinking the inspection part of manufacturing process, and prevents errors from turning into defects.
Poka-yoke means “defect prevention”. Poke- yoke system uses 100% inspection to guard against unavoidable human error.

30. Source Inspection
FIGURE Source inspection involves defect prevention; that is, preventing errors from turning into defects. (Source: Achieving Zero Defects Mistake-Proofing—The Zero Quality Control System, © 1999, Productivity, Inc. All rights reserved.

31. Common Errors

32. Chapter 36 (for review purpose, not required to turn in)
Review Questions:
13, 16, 27, 28 (page 997)
Problems: 6 (page 999)
Final Exam:
Date: April 26, 2011 (Tuesday)
Time: 12:00 pm– 2:00pm
Classroom: EC 2410