Macro to nano controlin plastics molding

David Kazmer, PE, PhDProfessor, University of Massachusetts Lowell

October 31st, 2008

Great Things for Akron• Goodyear Headquarters to stay• Prof. Kennedy’s 100 patents• Dean Cheng to National Academy of

Engineering

• PolymerEngineeringis vital

Is U.S. Manufacturing in Decline?

1950 1960 1970 1980 1990 2000 20100

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35

Year

Man

ufac

turin

g E

mpl

oym

ent

(% o

f U

S W

orkf

orce

)

Is U.S. Manufacturing in Decline?

1950 1960 1970 1980 1990 2000 20100

100

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900

Year

Man

ufac

turin

g ou

tput

(%

of

Y19

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utpu

t)

U.S. Manufacturing Productivity

1950 1960 1970 1980 1990 2000 2010

1

1.1

1.2

1.3

1.4

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1.9

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Year

Out

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or C

ost

(Y20

00=

100%

)

US Industry Historical Data

Historical 0.8% Productivity IncreaseRecent 1.5% Productivity Increase

Manufacturing Competitiveness • Manufacturers need 1.5% annual productivity

gains to remain competitive

Cost CategoryTypical

PlantOverseas

PlantAutomated

Plant

Direct materials (resin, sheet, fasteners, etc.) 0.50 0.48 0.50

Indirect material (supplies, lubricants, etc.) 0.03 0.03 0.03

Direct labor (operators, set-up, supervisors, etc.) 0.25 0.08 0.05

Indirect labor (maintenance, janitorial, etc.) 0.05 0.05 0.02

Fringe benefits (insurance, retirement, vacation, etc.) 0.07 0.03 0.03

Other manufacturing overhead (rent, utilities, machine depreciation, etc)

0.10 0.08 0.10

Shipping (sea, rail, truck, etc.) 0.00 0.05 0.00

“Landed” product cost 1.00 0.80 0.73

Manufacturing Competitiveness

10,000 m2

500 m2

DLH IndustriesCanton, OH

Fawer VisteonChangchun, China

ObsoleteCompetitive

Some Manufacturing Research• Macro control

– Real time polymer melt pressure control

• Nano control– Polymer self-assembly

with a functionalizedsubstrate

The Molding Process

Conventional Molding

BarrelHeaters

Reciprocating Screw

Check valveInjectionCylinder

ClampingCylinder

Operator Interface

Stationary PlatenMoving PlatenMold

Pellets

PolymerMelt

Process ControllerHydraulic

Power Supply

Clamping Unit Injection Unit

Tie Rods

• Limited control– Static mold geometry – Open loop process w.r.t.

polymer– So use simulation to

optimize design

Dynamic Feed• System to control

polymer melt in real time– Sensors to monitor

pressure– Movable valve to adjust

flow restriction– Servo control of valve

position from closed loop controller

Dynamic Feed

Dynamic Feed• Two primary issues

– Cost• Pressure transducers for feedback control• Hydraulic servovalves or large servomotors• Increased size of mold components

– Reliability• Pressure transducer longevity & drift• Hydraulic hoses & cylinders

– Too much control energy

Self-Regulating Valve Design

cylinder

valve

Intensification Ratio 100A

A

• Two significant forces:– Top: control force – Bottom: pressure force

• Forces must balance– Pin moves to equilibrium– Melt pressure is proportional to control force– Intensification factor related to valve design

– With high intensification ratio, able to:» Use low cost pneumatic or motors» Eliminate pressure transducers & controller

3D Flow Analysis

Pin Positioning

0

2

4

6

8

10

12

14

16

18

0 0.5 1 1.5 2 2.5 3

Pin Position (mm)

Pre

ssu

re d

rop

(M

Pa

)Q=1cc/secQ=5cc/secQ=25cc/sec

Scaling Laws

0

2

4

6

8

10

12

0 2 4 6 8 10 12

Valve Outer Diameter (mm)

Pre

ssu

re D

rop

(M

Pa

)

2.5 mm

5 mm 10 mm

5.4

690

P

Validation• All validation was performed with a

two cavity hot runner mold– Mold Masters Ltd (Georgetown, Ontario)

• Mold produced binder separators– 1.8 mm thick by 300 mm long– 10 g weight

• Three control schemes investigated– Convention molding– Open loop control– Closed loop control

with pressure feedback

Open Loop Pressure Control

0

5

10

15

20

25

30

35

40

0 2 4 6 8 10

Cylinder Air Pressure (V)

Mel

t P

ress

ure

(MP

a)Cavity 1, Hyd=400, Air=50

Cavity 1, Hyd=800, Air=50

Cavity 1, Hyd=400, Air=85

Cavity 1, Hyd=800, Air=85

Saturated melt pressure

Process Sensitivities

Conventional Weight

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

Conventional W

eig

ht

Conventional Molding

Open Loop Weight

7.7

7.8

7.9

8

8.1

8.2

8.3

8.4

Open L

oop W

eig

ht

Open Loop Melt Valve

• Use of valves reduced both machine sensitivity (main effects) and intra-run variation (whiskers)

Product Consistency

Processing RelativeVariable Variance Valve Gates Open Loop Closed Loop Valve Gates Open Loop Closed LoopMelt Temp 0.0025 0.1479 0.0240 0.0487 5.47E-05 1.44E-06 5.92E-06Mold Temp 0.0025 0.0812 -0.0082 0.0319 1.65E-05 1.66E-07 2.54E-06Inj Pres 0.0025 0.0308 0.0065 0.0109 2.37E-06 1.06E-07 2.99E-07Inj Velocity 0.0025 0.0000 -0.0211 -0.0818 1.66E-12 1.11E-06 1.67E-05Pack Pres 0.0025 0.2667 0.0158 0.0176 1.78E-04 6.21E-07 7.72E-07Pack Time 0.0025 0.1348 0.0826 0.0589 4.55E-05 1.71E-05 8.67E-06

Estimated long run standard deviations (g) 0.0172 0.0045 0.0059

Estimated short term standard deviations (g) 0.0096 0.0039 0.0078

Estimated total standard deviations (g) 0.0197 0.0060 0.0098

Relative process capability, Cp 1.000 3.806 2.915

VariancesSensitivities

2

2

1

m

jj j

dyxy dx

• Significant increase in process capability index

6P

USL LSLC

Flexibility Example• Switch mold inserts to make different cavities

– Varying sizes & thicknesses• Use pressure

valve to controlweights & size

Set max pressure and times for packing stage

Design mold withmultiple valves

For each zoneSet valve to

fully open, closeother valves.

Determine bestmachine settings

for one zone

Add all flow ratesand shot sizes for

filling stage

Mold with optimalsettings for

all zones

Optimalmoldings?

Adjust individual zones

7.7

7.8

7.9

8

8.1

8.2

8.3

8.4

0 5 10 15 20 25 30

Time (min)

Big

Pa

rt W

eig

ht (

g)

0

0.5

1

1.5

2

2.5

Pro

cess

Ca

pab

ility

Ind

ex,

Cp

k.

Large Cavity Control• Adjustments 2, 5, & 6 made for large cavity

– More melt flow and cavity pressure

Small Cavity Control• Adjustments 1, 3, 4, & 6 made for small cavity

– High melt flow rate but lower maximum pressure

6.08

6.09

6.1

6.11

6.12

6.13

6.14

6.15

0 5 10 15 20 25 30

Time (min)

Litt

le P

art

We

igh

t (g

)

0

0.5

1

1.5

2

2.5

Pro

cess

Ca

pa

bili

ty In

de

x, C

pk.

Pre

ssur

e (M

Pa)

100

80

60

40

20

0

0 5 10 15 20 25 Time (s)

Pressure Profile Phasing• The filling of each cavity may be offset in time• By offsetting pressures, the moment of

maximum clamp force is offset• Slight extensions in cycle time can yield drastic

reductions in clamp tonnage

Pre

ssur

e (M

Pa)

100

80

60

40

20

0

0 5 10 15 20 25Time (s)

Pre

ssur

e (M

Pa)

100

80

60

40

20

0

0 5 10 15 20 25Time (s)

Machine Optimization• Machine requirements can be greatly reduced

by optimizing and decoupling each zone

20

25

30

35

40

45

50

24.5 25 25.5 26 26.5 27 27.5

Cycle Time (sec)

Ton

nage

Pre

ssur

e (M

Pa)

100

80

60

40

20

0

0 5 10 15 20 25Time (s)

Pre

ssur

e (M

Pa)

100

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0 5 10 15 20 25Time (s)

Pre

ssur

e (M

Pa)

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0 5 10 15 20 25Time (s)

Pre

ssur

e (M

Pa)

100

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40

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0

0 5 10 15 20 25Time (s)

Summary• The concept of adding degrees of freedom to

polymer processing is very powerful– Real-time melt control is one example– Many other examples exist

Some Manufacturing Research• Macro control

– Real time polymer melt pressure control

• Nano control– Polymer self-assembly

with a functionalizedsubstrate

Flory-Huggins Free Energy• The bulk free energy

i: lattice volume fraction of component i

– ij : interaction parameter of i and j

– mi : degree of polymerization of i

– R : gas constant– T : absolute temperature

Phase diagram of ternary blends

Unguided

Template directed assembly

Highly ordered structures

Polymer A Polymer B

Template Guided Polymer Assembly

Fundamentals

• The total free energy of the ternary system (Cahn-Hilliard equation),

– F : total free energy– f : local free energy

– Ci : the composition of component i

– i: the composition gradient energy coefficient

• The mass flux, Ji is:

– Ci: Composition of component i

– Mi: is the mobility of component i

– i: is the chemical potential of component i

• This leads to a system of 4th order PDEs:

Mass FluxFundamentals

Numerical Method

• Discrete cosine transform method for PDEs

– and are the DCT of and – is the transformed discrete Laplacian,

Simulation Parameters

Validation Experiments• Chemically heterogeneous substrate on Au surface

– Ebeam lithography followed by self-assembly of alkanethiol monolayer– Hydrophylic strips covered by 11-Amino-1-undecanthiol (NH2)– Hydrophobic strips covered by 1-octadecanethiol (ODT)

• Ternary system of polymers used– Polyacrylic acid (PAA): Negative static electrical force– Polystyrene (PS): Hydrophobic– Dimethylformamide (DMF): Solvent, on the order of 98% volume

• Experimental procedure– Polymer solution placed on substrate by pipette – 6 minutes quiescence at room temperature and low humidity– Polymer solution spin coated at varying RPM for in 30 seconds

Validation Experiments• Investigated factors

– Spin coating RPM: 100, 3000, and 7000 RPM– Pattern substrate width: 100 to 1000 nm– PS/PAA ratio: 30/70, 50/50, 70/30– PAA molecular weight: 2k, 50k, 450 k

• Image acquisition– Field emission scanning electron microscopy (JEOL 7401)– Atomic force microscopy (non-contact mode, Veeco NanoScopella)– Fourier transform analysis (PSIA, v. 1.5)

• Model parameters then tuned by inspection of experimental and simulation results

Evolution of Domain Size, R

– The domain size, R(t), is proportional to t1/3

Phase Separation with Solvent Evaporation

Lz=L0-L·exp(-a*t), where t is the time, a is a constant, and Lz is the thicknessof the film at time t, and L0 is the thickness at t=0

Polymer 1 Polymer 2 Solvent

Time

Determination of M and

After comparison of the simulation and the experimental results

M=3.63·10-21 m5/(J*s)=1.82·10-7J/m

Experimental condition:• Spin coating speed: 3000 rpm• Time: 30 seconds• Solvent w%: 99%• PS/PAA (weight) : 7:3

Characteristic length, R=0.829m

Experiment

Experiment

Different Pattern Strip Widths

The simulation results generally matches the experimental behavior The pattern size has to match the intrinsic

domain size

Different PS:PAA Weight Ratios

The volume ratio of PS/PAA has to match the functionalized pattern area ratio

Effects of PAA Molecular Weight

The molecular weight of PAA will affect the shape of the Flory-Huggins local free energy Smaller molecular weight results in a more compatible pattern

Summary

3D simulation for ternary polymer system is established The evolution mechanism is investigated, with the

evolution of the domain proportional to t1/3 The condensed system has a faster agglomeration pace.

Simulation is validated by the experimental results Parameters are estimated, such as the mobility and gradient

energy coefficient. Effects of experimental factors are investigated.

The numerical results matches the experimental results in general, and the model can be used to assist the experiment and theoretical work. Incorporation with high rate plastics manuacturing is the

next focus.

Conclusions

The United States is no longer the R&D super power US R&D was 30% of global R&D in 1970 US R&D is now only 10% of global R&D These facts do not indicate that the US in in decline, but

rather that the rest of the world has made progress Manufacturing will remain a vital source of wealth creation

Competitive advantages are still evolving Natural and human resources

Logistical access to end-users US manufacturers must continue focused R&D

New product innovation Process productivity improvements Employee recruitment, growth, & retention

Acknowledgements

• Melt Control Research• Dynisco, Synventive, Mold-Masters• National Science Foundation (grant #NSF-0245309)

• Simulation of Polymer Self Assembly • Centre of High rate Nano-manufacture at UMass Lowell• National Science Foundation (grant #NSF-0425826)

• Prof. Isayev and the University of Akron

The Effects of the Rotation Speed

The faster rotation speed results in a smaller R value, due to the effects of the faster solvent evaporation

Validation with the Experiments-- with the Patterned Substrate

Measure of the compatibility parameter, Cs

Experiment: SEM images are compared with the template patterns

Simulation: Comparison of result pattern and substrate template are compared element by element

s1(k) - the parameter in the surface energy expression for polymer oneSk - the quantitative representation of the substrate attraction.

, and the greater the better

Determination of Controlling Factors• Huggins Interaction parameter,

– 12,C

: critical interaction parameter. 12,C

for spinodal

decomposition to occur.

– Determines the miscibility of the polymer pair

– Bigger D. P., easier phase separation

–

–

– i: solubility parameter of component I

– The difficulties to obtain accurate solubility parameters.

Determination of Controlling Factors

• Gradient energy coefficient,

– a : Monomer size, the affecting radius of van de

Waals force– Determines the domain size and interface thickness– – D: Diffusivity– Determines the kinetics of the phase transaction.

The values of k and D are estimated by benchmarking with the experimental results, as shown later.