Injection Moulding Technology Part 3 Quality

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

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.

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 ?

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

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.

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

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.

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

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

Normal distribution curves x

Normal distribution curves 6 x std dev (6 Sigma) = 99.9997% x

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

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

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

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

Minimum capability Cm = 1.67 or greater LSL USL

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

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

Worked example Cmk = 1.67 or greater Cmk = Difference between Avg. and nearest Limit 0.215 = = 2.04 3 x 0.035 (Sigma) 0.105 0.215 3 x sigma Cmk = 1.67 or greater LSL USL TARGET Avg.(Mean)

Injection Moulding Technology Part 3 Quality