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**Injection Moulding Technology**

Part 3 Quality

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

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**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.

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**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 ?

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**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

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**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.

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**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

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**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.

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**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

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**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

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**Normal distribution curves**

x

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**Normal distribution curves**

6 x std dev (6 Sigma) = % x

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**Why choose 6 sigma? 1 sigma = 691,462 DPM or 30.9% Defect free**

x

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

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**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.

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**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.

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Minimum capability Cm = 1.67 or greater LSL USL

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**but some samples are outside limits.**

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

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**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

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**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)

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**Injection Moulding Technology**

Part 3 Quality

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To Accompany Russell and Taylor, Operations Management, 4th Edition, 2003 Prentice-Hall, Inc. All rights reserved. Chapter 15 Statistical Quality Control.

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