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

Priamus Users’ MeetingOctober 5th, 2005

David Kazmer

Motivation

Optimize molding processesFaster set-upFaster cycle timesHigher quality & fewer rejects

Automatic quality assurance100% fully automatic cyclesHuge labor savings

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

Introduction tothe Instrumented

Molding Cell

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

Close-up of Instrumented Mold

Close-up of Instrumented Mold

Close-up of eDAQ

Sensor LocationsTemperature 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

Part with Sensor Locations

Process Data – Full CycleFilling Packing Cooling

Gate freeze-off

Mold closed

Part 1) Interpretation of an Instrumented

Molding Cell

How do I interpret a cavity pressure trace? Filling

Packing

4R

LQP

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40

Time (sec)

Pre

ssur

e (b

ar)

P_sprue

P_step

P_tensile

P_impact

4R

LQP flowup

4R

LQPP flowupdown

How do I interpret a cavity temperature trace?

Heat Transfer

Q high during filling k low during packing

Qdx

Td

C

k

dt

dT

P

2

2

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51

53

55

57

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61

63

65

0 10 20 30 40

Time (sec)

Tem

pera

ture

(C

)

T_tensile

T_impact

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 quicklyat end of pack

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50

Time (sec)

Pre

ssur

e (b

ar)

P_sprue

P_step

P_tensile

P_impact

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

45

47

49

51

53

55

57

59

61

63

65

0 10 20 30 40 50

Time (sec)

Tem

pera

ture

(C

)

T_tensile

T_impact

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

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40 50

Time (sec)

Pre

ssur

e (b

ar)

P_sprue

P_step

P_tensile

P_impact

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

45

47

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51

53

55

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61

63

65

0 10 20 30 40 50

Time (sec)

Tem

pera

ture

(C

)

T_tensile

T_impact

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 meaningfulS/N=625!

Qdx

Td

C

k

dt

dT

P

2

2

40

45

50

55

60

65

70

75

0 20 40 60 80 100

Injection Velocity (mm/sec)

Tem

pera

ture

Sen

sor

Out

put (

C)

AverageDuringPacking

MaximumAfter Fill

Peak cavity pressure indicates over-fill

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50

Time (sec)

Pre

ssur

e (b

ar)

P_sprue

P_step

P_tensile

P_impact

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50

Time (sec)

Pre

ssur

e (b

ar)

P_sprue

P_step

P_tensile

P_impact

Can pressure sensors detect an overfill condition?

Not really, peak temp indicates melt temp

40

45

50

55

60

65

70

75

0 10 20 30 40

Time (sec)

Tem

pera

ture

(C

)

T_tensile

T_impact

40

45

50

55

60

65

70

75

0 10 20 30 40

Time (sec)

Tem

pera

ture

(C

)

T_tensile

T_impact

Hypothesis: Slope is indicative of rate of heat transfer, and possible thickness/flashing?

?

Can temperature sensors detect an overfill condition?

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 offTrace for stepped

part follows sprue Gate not frozen off

0

20

40

60

80

100

120

140

160

0 10 20 30 40

Time (sec)

Pre

ssur

e (b

ar)

P_sprue

P_step

P_tensile

P_impact

Not usually Heat Transfer

Q,k≠f(P)

In this extreme case, parts shrinkfrom wall so low Q

Qdx

Td

C

k

dt

dT

P

2

2

45

47

49

51

53

55

57

59

61

63

65

0 10 20 30 40

Time (sec)

Tem

pera

ture

(C

)

T_tensile

T_impact

Can temperature sensors detect under packing?

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

Part 2) Optimization of an Instrumented

Molding Process

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!

If only one sensor is used, what/where should it be? One sensor is not sufficient

Lack of observability Recommend:

Screw positionNozzle/hydraulic pressureCavity pressure sensor near gateTemperature sensor at end of fill

Together, a single control strategy may be able to satisfy many molding applicationsFamily molds & multi-gated/cavity molds?

Setup of molding machine1. 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-off5. Perform a packing pressure vs. cooling time study to find minimum

cooling time6. 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

1. Short shot study at constant ram velocity Find required shot size

90 mm plastication20 mm switchover point10 mm cushion

Cushion could be reduced, but shot size is OK

2. 1st Stage Optimization Adjust VP transfer point for melt to reach

key junction2 mm stroke

All pressures about the sameSmall lengthLarge diameter

50 mm/sec selected

0

1

2

3

4

5

6

7

8

9

10

0 0.05 0.1 0.15 0.2

Time (sec)

Pre

ssur

e (b

ar)

12 mm/sec

25 mm/sec

50 mm/sec

2. 2nd Stage Optimization Adjust VP transfer point for melt to reach

next key junction2 mm first stageNext 28 mm stroke

Optimize velocity12 mm/sec25 mm/sec50 mm/sec100 mm/sec

Optimization Criterion:Integral of pressure (energy) Pressure varies with velocity

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4

Time (sec)

Pre

ssur

e (b

ar)

12 mm/sec

25 mm/sec

50 mm/sec

100 mm/sec

0

10

20

30

40

50

60

70

80

0 20 40 60 80 100 120

Injection Velocity (mm/sec)

Inte

gral

Pre

ssur

e (B

ar s

)

50 mm/sec is best.

2. 3rd Stage Optimization Adjust VP transfer point for melt to reach

next key junction2 mm first stageNext 28 mm stroke

Optimize velocity12 mm/sec25 mm/sec50 mm/sec100 mm/sec

0

50

100

150

200

250

300

0 20 40 60 80 100 120

Injection Velocity (mm/sec)

Inte

gral

Pre

ssur

e (B

ar s

)

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5

Time (sec)

Pre

ssur

e (b

ar)

12 mm/sec

25 mm/sec

50 mm/sec

100 mm/sec

Optimization Criterion:Integral of pressure (energy) Pressure varies with velocity

100 mm/sec is best.

Setup of molding machine1. 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-off5. Perform a packing pressure vs. cooling time study to find minimum

cooling time6. 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 assuranceFurther development warranted & on-going.

What does the future look like? Technology trends

Better, smaller, and cheaper sensorsHigher precision and faster data acquisitionCheaper & faster computers/storage

Application trendsMore applications will use sensors & DAQAutomated control will improve, providing

More capability & lower barrier to entry

Outsourcing will plateau, limited by Capability, infrastructure, shipping & other costs

Acknowledgements

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