Transcript Document

Macro to nano control
in plastics molding
David Kazmer, PE, PhD
Professor, 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
• Polymer
Engineering
is vital
Is U.S. Manufacturing in Decline?
Manufacturing Employment (% of US Workforce)
35
30
25
20
15
10
5
0
1950
1960
1970
1980
Year
1990
2000
2010
Is U.S. Manufacturing in Decline?
900
Manufacturing output (% of Y1950 Output)
800
700
600
500
400
300
200
100
0
1950
1960
1970
1980
Year
1990
2000
2010
U.S. Manufacturing Productivity
2
Output per Unit of Labor Cost (Y2000=100%)
1.9
1.8
US Industry Historical Data
Historical 0.8% Productivity Increase
Recent 1.5% Productivity Increase
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1
1950
1960
1970
1980
Year
1990
2000
2010
Manufacturing Competitiveness
• Manufacturers need 1.5% annual productivity
gains to remain competitive
Cost Category
Typical
Plant
Overseas
Plant
Automated
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
Obsolete
Competitive
Fawer Visteon
Changchun, China
DLH Industries
Canton, OH
500 m2
10,000 m2
Some Manufacturing Research
• Macro control
– Real time polymer melt
pressure control
• Nano control
– Polymer self-assembly
with a functionalized
substrate
The Molding Process
Conventional Molding
• Limited control
Stationary Platen
Moving Platen
Clamping
Cylinder
Tie Rods
Mold
Operator Interface
Pellets
–Check
Static
valve mold geometry
Injection
Reciprocating
Cylinder
Screw
– Open loop process w.r.t.
polymer
– So use Barrel
simulation to
Polymer
Melt
Heaters
optimize
design
Process Controller
Clamping Unit
Injection Unit
Hydraulic
Power Supply
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
• 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
Intensification Ratio 
Acylinder
Avalve
 100
– With high intensification ratio, able to:
» Use low cost pneumatic or motors
» Eliminate pressure transducers & controller
3D Flow Analysis
Pin Positioning
18
Q=1cc/sec
Q=5cc/sec
Q=25cc/sec
16
Pressure drop (MPa)
14
12
10
8
6
4
2
0
0
0.5
1
1.5
Pin Position (mm)
2
2.5
3
Scaling Laws
12
 2.5 mm
Pressure Drop (MPa)
10
8
 10 mm
 5 mm
6
4
2
0
0
2
4
6
8
10
Valve Outer Diameter (mm)
P 
12
690

4.5
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
40
Cavity 1, Hyd=400, Air=50
Cavity 1, Hyd=800, Air=50
35
Cavity 1, Hyd=400, Air=85
Melt Pressure (MPa)
30
Cavity 1, Hyd=800, Air=85
25
20
Saturated melt pressure
15
10
5
0
0
2
4
6
Cylinder Air Pressure (V)
8
10
el
t
el
t
Te
m
Conventional Molding
el
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Te
m
p
Lo
Te
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m
M
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Pr
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Pa
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Pa ime
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Hi
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M
Open Loop Weight
7.4
el
t
Conventional Weight
7.5
M
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In
j V ity
L
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ity
Pa
Hi
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Pr
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Pa
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Lo
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Pr
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Pa
s
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T
Pa ime
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Hi
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M
M
Process Sensitivities
• Use of valves reduced both machine sensitivity
(main effects) and intra-run variation (whiskers)
7.8
Conventional Weight
8.4
Open Loop Weight
7.7
8.3
7.6
8.2
8.1
8
7.3
7.9
7.2
7.8
7.1
7.7
Open Loop Melt Valve
Product Consistency
• Significant increase in process capability index
USL  LSL
CP 
6
Processing
Variable
Melt Temp
Mold Temp
Inj Pres
Inj Velocity
Pack Pres
Pack Time
Relative
Variance
0.0025
0.0025
0.0025
0.0025
0.0025
0.0025
Valve Gates
0.1479
0.0812
0.0308
0.0000
0.2667
0.1348
 dy
  

y
j 1  dx j
m
2
 2
  x j

Sensitivities
Variances
Open Loop
Closed Loop Valve Gates Open Loop
Closed Loop
0.0240
0.0487
5.47E-05
1.44E-06
5.92E-06
-0.0082
0.0319
1.65E-05
1.66E-07
2.54E-06
0.0065
0.0109
2.37E-06
1.06E-07
2.99E-07
-0.0211
-0.0818
1.66E-12
1.11E-06
1.67E-05
0.0158
0.0176
1.78E-04
6.21E-07
7.72E-07
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
Flexibility Example
• Switch mold inserts to make different cavities
– Varying sizes & thicknesses
• Use pressure
valve to control
weights & size
Design mold with
multiple valves
For each zone
Set valve to
fully open, close
other valves.
Add all flow rates
and shot sizes for
filling stage
Set max pressure
and times for
packing stage
Mold with optimal
settings for
all zones
Optimal
moldings?
Adjust individual
zones
Determine best
machine settings
for one zone
Large Cavity Control
• Adjustments 2, 5, & 6 made for large cavity
– More melt flow and cavity pressure





2.5

8.3
Big Part Weight (g)
2
8.2
1.5
8.1
8
1
7.9
0.5
7.8
7.7
0
0
5
10
15
Time (min)
20
25
30
Process Capability Index, Cpk.
8.4
Small Cavity Control
• Adjustments 1, 3, 4, & 6 made for small cavity
– High melt flow rate but lower maximum pressure





2.5

6.14
Little Part Weight (g)
2
6.13
1.5
6.12
6.11
1
6.1
0.5
6.09
6.08
0
0
5
10
15
Time (min)
20
25
30
Process Capability Index, Cpk.
6.15
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
Pressure (MPa)
100
80
60
40
20
0
0
5
10
15
Time (s)
20
25
Machine Optimization
• Machine requirements can be greatly reduced
by optimizing and decoupling each zone
Pressure (MPa)
100
50

80

60
40
45
20
0
0
5
15
Time (s)
20
25
40

100
80
Tonnage
Pressure (MPa)
10
60
40
20
0
0
5
15
Time (s)
20

25

100
Pressure (MPa)
10
35
30

80
60
25
40
20

0
0
5
10
15
Time (s)
20
25
20
24.5
25
25.5
26
Cycle Time (sec)
26.5
27
27.5
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 functionalized
substrate
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
Template Guided Polymer Assembly
Polymer A
Polymer B
Unguided
Template directed assembly
Highly ordered
structures
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
Fundamentals
Mass Flux
• The mass flux, Ji is:
– Ci: Composition of component i
– Mi: is the mobility of component i
– mi: is the chemical potential of component i
• This leads to a system of 4th order PDEs:
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
Time
Polymer 1
Lz=L0-L·exp(-a*t), where t
is the time, a is a constant,
and Lz is the thickness
of the film at time t, and L0
is the thickness at t=0
Polymer 2
Solvent
Determination of M and 
Experiment
Experimental condition:
• Spin coating speed: 3000 rpm
• Time: 30 seconds
• Solvent w%: 99%
• PS/PAA (weight) : 7:3
Characteristic length, R=0.829mm
Experiment
After comparison of the
simulation and the
experimental results
M=3.63·10-21 m5/(J*s)
=1.82·10-7J/m
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 one
Sk - the quantitative representation of the substrate
attraction.
, and the greater the better
Determination of Controlling Factors
• Huggins Interaction parameter, 
– 12,C : critical interaction parameter. c>c12,C for spinodal
decomposition to occur.
– Determines the miscibility of the polymer pair
– Bigger D. P., easier phase separation
–
–
– di: 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.