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Injection Moulding Technology - Part 3

CoWC 0410

Injection Moulding Technology

Part 3

Quality


CoWC 0410

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

Session aim


CoWC 0410

By the end of the session you will be able to:

Calculate Cm and Cmk values.

Explain how weight can be used to monitor the process.

State 3 Quality Improvement tools.

Session objectives


CoWC 0410

The manufacturing process

Methods People Material

Environment Equipment

Output

Information on quality

Decisions taken

Quality control - Detection systems


CoWC 0410

ImproveDesigns Update

PerformanceDoE

Information on qualitySPC

FMEA

The manufacturing process

Method People Material

Environment Equipment

Output

Decisions taken

Quality assurance - Prevention systems


CoWC 0410

Quality tools

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)

SPC (Statistical Process Control)

A mathematical technique to measure and improve performance.


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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

Failure Mode & Effects Analysis


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SPC - More detail

Collecting, representing and analysing data,developing and understanding patterns.

A sequence of operations, not only themachine cycle.

Statistical

Process

Control

Measuring performance, taking action onthe data.


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TargetBottom

LimitTopLimit

Total Tolerance

x x xx x x x x x

Terminology

xx


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Measured sizes

Target piston diameter = 60 mm (+/- 1mm)

Case study

60.0

x x x x xx xx x xx

xx

x

61.2

60.8

60.6

60.4

60.2

61.0

58.8

59.8

59.4

59.2

59.0

59.6

Target

Total Tolerance

xx

xx xx

x xx x

x

x

xx

xx

xx

x

xx

x x x

xxx

xx

x

xxx

x

xx

Total Variation in Sample


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Normal distribution curves

x


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Normal distribution curves

x

6 x std dev (6 Sigma) = 99.9997%


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Why choose 6 sigma?

x

1 sigma = 691,462 DPM or 30.9% Defect free




6 sigma = 3.4 DPM or 99.9997% Defect free


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Machine capability

Total tolerance6 x Sigma

CmCm =

CmCm = Measure of the variation present, in relation to the available tolerance.


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Capability = Cm = Total tolerance = 1.2 = 36 x Sigma 0.4

Curve fits into tolerance 3 times.

High capability

Tolerance = +/- 0.6

6 Sigma

( = 0.4 )


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Curve does not fit inside the tolerance.

Low capability

Capability = Cm = Total tolerance = 1.2 = 0.756 x Sigma 1.6

Tolerance = +/- 0.6

6 Sigma

( = 1.6 )


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LSL USL

Cm = 1.67 or greaterCm = 1.67 or greater

Minimum capability


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LSL USL

Cm is still = 1.67

Minimum capability

but some samples are outside limits.

Target


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Targeting

CmkCmk = Difference between the Avg. and nearest limit

3 x Sigma

CmkCmk = Measure of the variation present in relation to the available tolerance, combined targeting of the set-up.


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LSL USL

Worked example

TARGETAvg.(Mean)

Cmk = Difference between Avg. and nearest Limit

3 x 0.035 (Sigma)=

Cmk = 1.67Cmk = 1.67 or greateror greater

0.215

0.105

3 x sigma

= 2.04

0.215


CoWC 0410

Injection Moulding Technology

Part 3

Quality

Download - Injection Moulding Technology

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