1. The Quality Control Function

2. Quality Control Can not be avoided Is expensive Optimum level required
Must be very clearly focussed

3. Lack of Focus Too much data Information too late Picture not clear
Poor decisions, unnecessary cost

4. Focus on Functions Reasons for quality control testing
Analyse for key control parameters
Establish minimum testing requirements
Document operations and procedures

5. Key Functions All have different requirements Customer service
Product monitoring
Process control
Product development
Investigations

6. Distinguish Between Performance Targets and Process Control Parameters
They are not the same and must not be confused

7. This is what we are supposed to deliver
Performance Targets
Are those fabric properties that the customer specifies e.g. Weight 150 gsm ± 5% Shrinkage not more than 6%
This is what we are supposed to deliver

8. This is how we achieve our Performance Targets
Control Parameters
Are those yarn and fabric properties, machine settings and process conditions which have to be held at constant levels to guarantee the Performance Targets
This is how we achieve our Performance Targets

9. Right-First-Time means no compromise in hitting Control Targets
Control Parameters
Examples:
Yarn Count
Course Length
Finished Course Density
Right-First-Time means no compromise in hitting Control Targets

10. Minimise the Amount of Testing
Test only what is strictly necessary
Shrinkage testing is not always necessary
…courses and width will serve
Grey fabric weight is not necessary
… it is not a control parameter
100% inspection is seldom effective
… it means that quality is out of control

11. Quality Control Testing
Should be a precision tool
For designated control parameters
At specific locations
For defined reasons
Data are not just for filing. They must have an immediate purpose

12. STARFISH Philosophy Product Quality and Performance are guaranteed by
Rational Product Design
Accurate Process Control
START as you mean to FINISH

13. Major Activities Identify critical processes
Establish control parameters
Define procedures to maintain control
Ensure proper operative training
Investigate how to improve control

14. Accurate Process Control
Requires a knowledge of the normal operating capability of the process
Determine Standard Deviations

15. Standard Deviation

16. Variability of Test Data25
Test data is never invariant
Frequency
distribution 20
There is always some variation 15
Frequency 10 5 27 28 29 30 31 32 33
Measured Value

17. Standard Deviation - Definition
SD is a measure of variability
Deviation of an individual measurement, Xi
= ( Xi - Mean )
Variance = mean (squares of deviations)
Standard Deviation = square root of Variance
Low Standard Deviation means
low variability in measurements

18. Standard Deviation - Calculation
for each measurement
SD =
find the deviation and square it
Sum ( Xi - Mean )2 N
add all the squares
find the mean of the squares and take the square root
Spreadsheets allow automatic calculation

19. SD and Frequency Distribution25
Low SD 20 15
Frequency 10
High SD 5 24 26 28 30 32 34 36
Measured Value

20. Standard Deviation - Interpretation
For a ‘normal’ distribution 25 20
~ 68% +/- 1sd 15
Normalised Deviation = Deviation / SD
Frequency
~ 95% +/- 2sd 10 5
~ 99.7% +/- 3sd
-3sd
-2sd
-1sd
Mean
+1sd
+2sd
+3sd
Normalised Deviation

21. SD and Process Control
Well-controlled processes deliver low Standard Deviations
SD contains all of the variations in
materials
methods
machinery
SD is an objective indication of the current level of control in the operation

22. Coefficient of Variation
Standard Deviation expressed as a percentage of the Mean
CV = SD / Mean
CV allows comparisons of variability to be made between properties that have different means

23. Standard Error SE = SD / square root ( N )
The more measurements we make the more reliable is the mean
Standard error is an indication of the reliability of the mean
SE = SD / square root ( N )

24. Standard Deviations Are fundamental to process control
They reflect the normal capability of a process
They determine the current limits of control
They comprise:
Assignable variation
Random variation

25. All assignable variations must be identified and held to a minimum
Example:
Variation in Yarn Count
causes variations in Fabric Weight
All assignable variations must be identified and held to a minimum

26. Random Variation
Variation which can not be assigned to specific causes
After assignable variations have been identified and reduced to their minimum, the sources of apparently random variation can often be identified.

27. Quality Control Charts
For monitoring Control Parameters
Show whether a process is in control
Can detect change or drift
Simple, quick, understandable display
Statistical Process Control

28. Control Chart Parameters
To construct a control chart we need to calculate:
The Target Value
The Normal Tolerance
The Action Tolerance

29. Target Value The Design Specification
This is the value that we hope to deliver, on average, over a long period of time.

30. Normal Tolerance Two Standard Deviations
If the deviation from the Target Value is less than the Normal Tolerance then the process is almost certainly operating within its normal capacity

31. Action Tolerance Three Standard Deviations
If the deviation from the Target Value is more than the Action Tolerance then the process is almost certainly operating outside its normal capacity

32. Quality Control Chart Outline
Action
T + 3SD
Warning
T + 2SD
Normal
Control Parameter Value
Target
Normal
T - 2SD
Warning
T - 3SD
Action
Time

33. Control Chart For Yarn Tex
23.0
Simulation: Mean = 19.7, CV = 3%
22.0
21.0
Measured yarn Count, tex
20.0
19.0
18.0
17.0 20 40 60 80
100
Observation No.

34. Control Chart For Yarn Tex
23.0
Supplier A: Mean = 20.2, CV = 2.2%
Supplier B: Mean = 19.2, CV = 2.0%
22.0
21.0
Measured yarn Count, tex
20.0
19.0
18.0
17.0 20 40 60 80
100
Observation No.

35. Supplementary Action Criteria
Gradual drift may not be obvious
Therefore, take action if:
two consecutive warnings, same side
a run of seven on one side
First action is:
make new measurements
confirm the action signal

36. Caution Variation is also contributed by Measuring instrument
Measurement procedure
Environment
Operator
Standardize procedures, calibrate equipment and train operators thoroughly.

37. Action Must be taken immediately Three general sources of problems
Machinery
Materials
Operator
Control charts can be displayed

38. Additional Uses Control Charts can also be used to:
Monitor design tolerances
Monitor customer tolerances
Optimise product design

39. Monitoring Tolerances
135
140
145
150
155
160
165 20 40 60 80
100
Target = 150 gsm, Tolerance = ± 5%
Actual Mean = gsm, CV = 2.8%
Measured Fabric Area Weight, gsm
Observation No.

40. Optimising Product Design
Target = 150 gsm, Tolerance = ± 5%
Actual Mean = gsm, CV = 2.0%
135
140
145
150
155
160
165 20 40 60 80
100
Measured Fabric Area Weight, gsm
Observation No.

41. Control Charts Manual