1. Instrumented Molding Cell - Part 1) Interpretation - Part 2) Optimization Priamus Users’ Meeting October 5 th, 2005 David Kazmer

2. Motivation Optimize molding processes  Faster set-up  Faster cycle times  Higher quality & fewer rejects Automatic quality assurance  100% fully automatic cycles  Huge labor savings

3. Frequently Asked Questions How do I interpret a cavity pressure trace? How do I interpret a cavity temperature trace? Which is better for detecting melt at end of flow? Can temperature sensors detect changes in melt temperature? Can these sensors detect an underfill condition? Can these sensors detect underpack condition? Can these sensors detect an overfill or overpack condition? How should we setup our molding machine (w.r.t ram velocity, transfer, etc.)? If only one sensor is used, what/where should it be? What does the future look like? Part 1 Part 2

4. Introduction to the Instrumented Molding Cell

5. Test Cell 50 ton Electra injection molding machine Instrumented mold  2 temperature sensors at end of flow  4 pressure transducers near gates Priamus eDAQ data acquisition system

6. Close-up of Instrumented Mold

8. Close-up of eDAQ

9. Sensor Locations Temperature Sensors: Temperature 1 – Tensile Test Bar, End of Fill Temperature 2 – Flexural Test Bar, End of Fill Pressure Sensors: Pressure 5 – Flexural Test Bar, Near Gate Pressure 6 – Primary/Secondary Runner Intersection Pressure 7 – Rectangular Stepped Plaque, Near Gate Pressure 8 – Tensile Test Bar, Near Gate Also adding: Ram position transducer Nozzle pressure transducer Digital input for switchover

10. Part with Sensor Locations

11. Process Data – Full Cycle Filling PackingCooling Gate freeze-off Mold closed

12. Part 1) Interpretation of an Instrumented Molding Cell

13. How do I interpret a cavity pressure trace? Filling Packing

14. How do I interpret a cavity temperature trace? Heat Transfer  Q high during filling  k low during packing

15. Which is better for detecting melt at end of flow? Pressure sensors may detect possible short shot if:  Cavity pressures are low at ‘fill’  Cavity pressures decay quickly at end of pack

16. Which is better for detecting melt at end of flow? Temperature sensors will indicate short shot if:  Melt doesn’t reach transducer  Impact specimen was short

17. Which is better for detecting melt at end of flow? Pressure transducer signal to noise ratio  Ramp rate: 5000 psi/s  Variation: 19 psi  Signal level: 100 psi  S/N ratio: ~5:1  Response time: 0.02 s With noise

18. Which is better for detecting melt at end of flow? Temperature sensor signal to noise ratio  Ramp rate: 465 C/s  Variation: 0.024 C  Signal level: 0.2 C  S/N ratio: 8.33  Response time:.001 s

19. Can temperature sensors detect changes in melt temperature ? Heat Transfer  Fast injection means high Q & low dt  Slow injection means low Q & high dt Max temperature is very meaningful  S/N=625!

20. Peak cavity pressure indicates over-fill Can pressure sensors detect an overfill condition?

21. Not really, peak temp indicates melt temp Hypothesis: Slope is indicative of rate of heat transfer, and possible thickness/flashing? ? Can temperature sensors detect an overfill condition?

22. Can pressure sensors detect over or under packing? Usually indicated by pressure at end of pack  Traces for tensile & impact specimens decay prior to end of pack Gate is frozen off  Trace for stepped part follows sprue Gate not frozen off

23. Not usually  Heat Transfer  Q,k≠f(P) In this extreme case, parts shrink from wall so low Q Can temperature sensors detect under packing?

24. FAQ Answers How do I interpret a cavity pressure trace?  Carefully, confounding of temperature, gate freeze, full cavity How do I interpret a cavity temperature trace?  Readily Which is better for detecting melt at end of flow?  Temperature, higher signal to noise ratio & response time Can temperature sensors detect changes in melt temperature?  Yes, by looking at the peak temperature sensed  This result is not 1:1, more modeling being done… Can these sensors detect an underfill condition?  Temperature: definitely, by no increase in local mold temperature  Pressure: sometimes, by looking at slopes after switchover Can these sensors detect an underpack condition?  Temperature: not usually, sometimes in extreme cases  Pressure: usually, by looking at cavity pressure decay Can these sensors detect an overfill or overpack condition?  Pressure: usually, by looking at peak cavity pressure  Temperature: not easily, but maybe Part 1

25. Part 2) Optimization of an Instrumented Molding Process

26. How should we setup our molding machine? Scientific molding is:  Necessary but not sufficient We can and need to do better  Integrated product, mold, and process design Developing mold designs that are fit for purpose, and Relating quality requirements to control strategies  Formal procedures for instrumentation & setup Lights out is only achieved in small minority of vertical applications of captive molders!

27. If only one sensor is used, what/where should it be? One sensor is not sufficient  Lack of observability Recommend:  Screw position  Nozzle/hydraulic pressure  Cavity pressure sensor near gate  Temperature sensor at end of fill Together, a single control strategy may be able to satisfy many molding applications  Family molds & multi-gated/cavity molds?

28. Setup of molding machine 1. Short shot study at constant ram velocity  Find required shot size 2. Start with single stage, no packing  Adjust VP transfer point for melt to reach key junction  Optimize one velocity step, similar to “scientific molding” Add additional stages for each juncture (position vs. velocity) 3. Find required pack pressure to satisfy tolerances, using long pack times 4. Find the minimum packing time for gate freeze-off 5. Perform a packing pressure vs. cooling time study to find minimum cooling time 6. Adjust mold/melt temperatures to verify long term stability  Collect parts & identify process fingerprints 7. Implement centered molding process, relying on human validation until process fingerprints & QA system are validated 8. Implement fully automatic quality assurance

29. 1. Short shot study at constant ram velocity Find required shot size  90 mm plastication  20 mm switchover point  10 mm cushion Cushion could be reduced, but shot size is OK

30. 2. 1 st Stage Optimization Adjust VP transfer point for melt to reach key junction  2 mm stroke All pressures about the same  Small length  Large diameter 50 mm/sec selected

31. 2. 2 nd Stage Optimization Adjust VP transfer point for melt to reach next key junction  2 mm first stage  Next 28 mm stroke Optimize velocity  12 mm/sec  25 mm/sec  50 mm/sec  100 mm/sec

32. Optimization Criterion: Integral of pressure (energy) Pressure varies with velocity 50 mm/sec is best.

33. 2. 3 rd Stage Optimization Adjust VP transfer point for melt to reach next key junction  2 mm first stage  Next 28 mm stroke Optimize velocity  12 mm/sec  25 mm/sec  50 mm/sec  100 mm/sec

34. Optimization Criterion: Integral of pressure (energy) Pressure varies with velocity 100 mm/sec is best.

35. Setup of molding machine 1. Short shot study at constant ram velocity  Find required shot size 2. Start with single stage, no packing  Adjust VP transfer point for melt to reach key junction  Optimize one velocity step, similar to “scientific molding” Add additional stages for each juncture (position vs. velocity) 3. Find required pack pressure to satisfy tolerances, using long pack times 4. Find the minimum packing time for gate freeze-off 5. Perform a packing pressure vs. cooling time study to find minimum cooling time 6. Adjust mold/melt temperatures to verify long term stability  Collect parts & identify process fingerprints 7. Implement centered molding process, relying on human validation until process fingerprints & QA system are validated 8. Implement fully automatic quality assurance Further development warranted & on-going.

36. What does the future look like? Technology trends  Better, smaller, and cheaper sensors  Higher precision and faster data acquisition  Cheaper & faster computers/storage Application trends  More applications will use sensors & DAQ  Automated control will improve, providing More capability & lower barrier to entry  Outsourcing will plateau, limited by Capability, infrastructure, shipping & other costs

37. Acknowledgements We wish to thank Priamus System Technologies for their generous support and excellent capabilities