- •1. TABLE OF CONTENTS
- •2. QUALITY CONTROL
- •2.1 INTRODUCTION TO QUALITY
- •2.2 PRACTICE PROBLEMS
- •3. STATISTICAL PROCESS CONTROL
- •3.1 CONTROL CHARTS
- •3.1.1 Sampling
- •3.1.2 Creating the Charts
- •3.1.3 Maintaining the Charts
- •3.1.4 The s-Chart
- •3.1.5 Interpreting the Control Charts
- •3.1.6 Using the Charts for Process Control
- •3.1.7 Practice Problems
- •3.2 CONTROL CHARTS FOR ATTRIBUTES
- •4. INSPECTION FOR QUALITY
- •4.1 ACCEPTANCE OF LOTS
- •4.2 SCREENING
- •4.3 THE COST OF SAMPLING
- •4.4 SINGLE/DOUBLE/MULTIPLE SAMPLED PLANS
- •4.5 OPERATING CHARACTERISTIC (OC) CURVES
- •4.5.1 Practice Problems
- •4.6 MIL-STD-105D AND ANSI/ASQC Z1.4-1981
- •4.6.1 Practice Problem
- •4.7 ANSERED QUESTIONS
- •4.8 UNANSWERED PROBLEMS
- •4.9 QUALITY CONTROL PROJECTS
- •4.9.1 Measuring for Quality Control
- •4.9.2 Evaluation of Metrology Equipment
- •4.10 ANSWERS TO SELECTED PROBLEMS:
- •4.11 QUALITY CONTROL FORMS
- •5. QFD (QUALITY FUNCTIONAL DEPLOYMENT)
- •5.1 REFERENCES
- •6. DETERMINING CAUSES OF PROBLEMS
- •6.1 CAUSE AND EFFECT DIAGRAMS
- •6.1.1 Process Diagrams
- •6.2 PARETO DIAGRAM
- •6.3 MATRIX ANALYSIS
- •7. TOTAL QUALITY CONTROL
- •8. W. EDWARD DEMING’S 14 POINTS
- •9. TOTAL QUALITY MANAGEMENT (TQM)
- •10. PROCESS CAPABILITY
- •11. SIX SIGMA QUALITY
- •12. DESIGN OF EXPERIMENTS
- •12.1 OVERVIEW
- •12.2 n-FACTORIAL
- •13. TAGUCHI METHODS
- •14. REFERENCES
- •15. DESIGN OF EXPERIMENTS
- •15.1 OVERVIEW
- •15.2 n-FACTORIAL
- •15.3 PRACTICE PROBLEMS
- •15.4 REFERENCES
- •17.1 PRACTICE PROBLEMS
page 73
e.g., Given the control chart for the a process, and a feature to be turned on the process, determine if the tolerances specified are reasonable.
2.0046” |
UCL |
2.000” |
+0.004” |
|
|
|
-0.000” |
|
X |
|
|
2.0028” |
|
|
|
2.0010” |
LCL |
|
|
|
|
|
( U – L) |
2.004 |
– 2.000 |
|
0.004 |
|
GOOD |
||
|
|
|
|
|
|
|
|||||
Cp |
= |
UCL-----------------------------– LCL = |
2.0046-------------------------------------- |
– 2.0010 |
= |
0.0036--------------- = 1.11 |
|
|
|||
Z1 |
= |
2.004 – 2.0028 |
= 0.667 |
|
Z2 = |
2.0028 – 2.000 |
= 1.556 |
||||
2.0046 – 2.0010 |
|
2.0046 – 2.0010 |
|||||||||
|
|
|
--------------------------------------2 |
|
|
|
|
|
-------------------------------------- |
|
|
|
|
|
|
|
2 |
|
|
||||
Cpk |
= min( 0.667, 1.556) |
= 0.667 |
|
|
|
|
|
BAD
•If Cp or Cpk are both too large (>>2) the process may be too good, suggesting less expensive, easier processes could be considered.
•A good rule of thumb for using Cp and Cpk is that if new equipment is being purchased, try to get values above 1.5, if the equipment is already available, 1.33 is acceptable.
•Try Probs #6 pg124, #32,33 pg128
11. SIX SIGMA QUALITY
•Developed by Motorola
•A strategy to target quality problems
page 74
• In simple terms,
Cp = 2
Cpk = 1.5
* Recall,
- if Cp = Cpk then X =µ , and the process is always centered
Cp = |
U------------– L |
|
|
|
|
|
|
6σ |
|
|
|
|
|
Cpk |
= U-------------– µ |
or |
µ------------ |
– L |
select the smaller of the two |
|
|
3σ |
|
|
3σ |
|
|
the mean typically drifts about |
1.5σ , as established by historical means |
and, +/-6σ quality gives 2 defaults in 1 million with no variation. with a 1.5σ variation in mean the reject rate is 3.4 in 1 million
process variance
|
|
|
|
|
X variance |
|
-3σ |
-2σ |
-σ |
µ |
σ |
2σ |
3σ |
|
|
|
|
|
|
•This can be summarized as 6σ of variance alone ignores the drift of the process centre. Assuming the process centre varies by +/-1.5σ results in a more accurate model of production.
•The steps in implementation are,
1.Identify critical characteristics through such functions and activities as marketing, industrial design, R&D engineering, etc.
2.Identify the product elements that influence the critical characteristics defined in step 1.
3.Define the process elements that influence the critical characteristics defined in step 2.
4.Establish maximum tolerances for each product and process element defined in steps 2 and 3.
5.Determine actual capability of the elements presented in steps 2 and 3.
6.Assume Cp >= 2 and Cpk >= 1.5.