1. Injection Moulding Technology
Part 3
Quality

2. Session aim
To improve the delegates understanding of quality issues, relating to injection moulding and how the process can be optimised and monitored.

3. Session objectives By the end of the session you will be able to:
State 3 Quality Improvement tools.
Explain how weight can be used to monitor
the process.
Calculate Cm and Cmk values.

4. Quality control - Detection systems
The manufacturing process
Methods
People
Material
Output
Information on quality
Decisions taken
Environment
Equipment
Smed,DOE,FMEA,JIT,SPC where do they fit in ?

5. Quality assurance - Prevention systems
The manufacturing process
Method
People
Material
Output
Decisions taken
Environment
Equipment
Smed,DOE,FMEA,JIT,SPC where do they fit in ?
SPC
Information on quality
Improve
Designs
FMEA
Update
Performance
DoE

6. Quality tools DoE (Design of Experiments)
A combination of trials to identify optimum
process conditions. e.g. L8 – 7 variables with 2 levels.
DoE (Design of Experiments)
Step-by-step approach to identify all possible failures in a design, manufacturing or assembly process.
FMEA (Failure Mode & Effects Analysis)
Explain how weight can be used to monitor the process.
SPC (Statistical Process Control)
A mathematical technique to measure and
improve performance.

7. Failure Mode & Effects Analysis
1. Process function – Capability study on m/c.
2. Potential failure modes – Zero cushion position
3. Potential effects of the failure – High scrap rate
4. Potential causes of the failure – Worn/damaged check ring
5. Current process controls – None
6. Recommended actions – a) Barrel tolerances +/- 100C b) Monitor cushion position
c) Visual check every 6 months (Abrasive polymers)
PC & GF grades

8. SPC - More detail Statistical
Process
Control
Collecting, representing and analysing data,
developing and understanding patterns.
A sequence of operations, not only the
machine cycle.
Explain how weight can be used to monitor the process.
Measuring performance, taking action on
the data.

9. x x x x xx x x x x x Terminology Total Tolerance Top Limit
Bottom Limit
x x xx x x x x x
Target

10. x x x Case study Target piston diameter = 60 mm (+/- 1mm) 58.8 59.0
Total Tolerance
Total Variation in Sample
Target x x x
58.8
59.0
59.2
59.4
59.6
59.8
60.0
60.2
60.4
60.6
60.8
61.0
61.2
Measured sizes

11. Normal distribution curvesx

12. Normal distribution curves
6 x std dev (6 Sigma)
= % x

13. Why choose 6 sigma? 1 sigma = 691,462 DPM or 30.9% Defect freex

14. Machine capability
Cm = Measure of the variation present, in relation to the available tolerance.
Total tolerance
6 x Sigma
Cm =

15. Capability = Cm = Total tolerance = 1.2 = 3
High capability
Capability = Cm = Total tolerance = 1.2 = 3
6 x Sigma
6 Sigma
( = 0.4 )
Tolerance = +/- 0.6
Curve fits into tolerance 3 times.

16. Low capability Capability = Cm = Total tolerance = 1.2 = 0.75
6 x Sigma
6 Sigma
( = 1.6 )
Tolerance = +/- 0.6
Curve does not fit inside the tolerance.

17. Minimum capability
Cm = 1.67 or greater
LSL
USL

18. but some samples are outside limits.
Minimum capability
Cm is still = 1.67
but some samples are outside limits.
Target
LSL
USL

19. Targeting Cmk = Measure of the variation present in relation
to the available tolerance, combined
targeting of the set-up.
Cmk =
Difference between the Avg. and nearest limit
3 x Sigma

20. Worked example Cmk = 1.67 or greater
Cmk = Difference between Avg. and nearest Limit
0.215 = =
2.04
3 x (Sigma)
0.105
0.215
3 x sigma
Cmk = 1.67
or greater
LSL
USL
TARGET
Avg.(Mean)

21. Injection Moulding Technology
Part 3
Quality