1 / 37

Instrumented Molding Cell - Part 1) Interpretation - Part 2) Optimization

Instrumented Molding Cell - Part 1) Interpretation - Part 2) Optimization. Priamus Users’ Meeting October 5 th , 2005 David Kazmer. Motivation. Optimize molding processes Faster set-up Faster cycle times Higher quality & fewer rejects Automatic quality assurance

urania
Download Presentation

Instrumented Molding Cell - Part 1) Interpretation - Part 2) Optimization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Instrumented Molding Cell- Part 1) Interpretation- Part 2) Optimization Priamus Users’ MeetingOctober 5th, 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. Part 1 Part 2 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?

  4. Introduction tothe InstrumentedMolding Cell

  5. Test Cell • 50 ton Electra injection molding machine • Instrumented mold • 2 temperature sensorsat end of flow • 4 pressure transducersnear gates • Priamus eDAQ data acquisition system

  6. Close-up of Instrumented Mold

  7. Close-up of Instrumented Mold

  8. Close-up of eDAQ

  9. Sensor Locations • Also adding: • Ram position transducer • Nozzle pressure transducer • Digital input for switchover 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

  10. Part with Sensor Locations

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

  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 pressuresdecay quicklyat end of pack

  16. Which is better for detecting melt at end of flow? • Temperature sensors will indicate short shot if: • Melt doesn’t reachtransducer • 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 meanshigh Q & low dt • Slow injection meanslow Q & high dt • Max temperature isvery meaningful • S/N=625!

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

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

  22. Can pressure sensors detect over or under packing? • Usually indicated by pressure at end of pack • Traces for tensile& impact specimensdecay prior to end ofpack • Gate is frozen off • Trace for steppedpart follows sprue • Gate not frozen off

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

  24. Part 1 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

  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 minorityof 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 • Short shot study at constant ram velocity • Find required shot size • 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) • Find required pack pressure to satisfy tolerances, using long pack times • Find the minimum packing time for gate freeze-off • Perform a packing pressure vs. cooling time study to find minimum cooling time • Adjust mold/melt temperatures to verify long term stability • Collect parts & identify process fingerprints • Implement centered molding process, relying on human validation until process fingerprints & QA system are validated • 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. 1st 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. 2nd 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. 3rd 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 • Short shot study at constant ram velocity • Find required shot size • 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) • Find required pack pressure to satisfy tolerances, using long pack times • Find the minimum packing time for gate freeze-off • Perform a packing pressure vs. cooling time study to find minimum cooling time • Adjust mold/melt temperatures to verify long term stability • Collect parts & identify process fingerprints • Implement centered molding process, relying on human validation until process fingerprints & QA system are validated • 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

More Related