presentation summary: sensors, control and automation group

Presentation Summary:Sensors, Control and Automation

Group

NSF/DOE/APC Workshop:

The Future of Modeling in Composites Molding Processes

June 9-10, 2004

Role of Modeling in Bridging the Science and Practice of Composites Processing

Ranga PitchumaniComposites Processing Laboratory

Department of Mechanical EngineeringUniversity of Connecticut

Storrs, Connecticut 06269-3139http://www.engr.uconn.edu/cml

NSF/DOE/APC Workshop: Future of Modeling in Composites Molding ProcessesSensing, Control and Automation Session

Arlington, VA • June 9–10, 2004

Composites Processing Laboratory, University of Connecticut

Agenda Items

Introduction Presentations (9:00–10:30am)

Pitchumani Coulter Griffith Glancey Hsiao Kennedy

Discussion and Summary Preparation (10:30–11:45am)


Outline

Control for Mold Filling in Liquid Molding Processes

Permeability Sensing Analysis and Design of Processes under

Uncertainty


Liquid Composite Molding Processes

Preform permeation is a critical step run-to-run variabilities voids and dry spots = part quality

Benefits of online model predictive control incorporates process physics; is robust and

effective however, needs rapid model prediction (real-time)

need for control

PreformPermeation

Curing

Preforming

Composite Product


Flow Modeling

resin

air0.0

1.0 1.0 1.0

0.80.7

0.20.0

0.5

x

y

uv

mold cavity ()

u x

px

,v y

py

Flow velocity through Darcy’s law

Boundary Conditions:

inlet ports = at a prescribed volumetric flowrate or at a prescribed pressure

exit vents = each at atmospheric pressure

mold walls = zero volumetric flowrate (impenetrable)

Continuity Eqn:

Flow progression obtained using a volume tracking method

ux

vy

0


Flow Control: Controller ArchitectureNielsen and Pitchumani, Polymer Composites (2002)

Yact(t)

Fuzzy Logic-based

Permeability Estimator

Fuzzy Logic-based


pro

cess con

troller

ANN-based flow simulator

SA-basedOptimizer

P(t) Y* (t+t)

desired flow scheme

Ydes(t+t)

p1 p2 p3

x

y on

line

flo

w s

enso

r

avg(t)

Pressure InjectionHardware


Preform

Resin

(t)

Popt(t)


Controller Implementation

Pressure controllers

vents

inlets

injectionguns

mold

air supply

D/A board

ControllerArchitecturein LabVIEW

FrameGrabber

CCD Camera


Real Time Control Movie

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.




Example: Race TrackingNielsen and Pitchumani, Polymer Composites (2002)

controlled

desired

p1

p2

p3

ind

uce

d r

acet

rack

ing

1 2 3 4 5 6 7 8

0 s

30 s

50 s

40 s

20 s

10 s

60 s

0

50

100

150

200

250p

1

p2

p3

Inle

t P

ress

ure

[kP

a]

0

2

4

6

8

10

0 10 20 30 40 50 60

Time [sec]

Lo

cal P

erm

ea

bili

ty [

x10-9

m2 ]


Example Controlled RunNielsen and Pitchumani, Polymer Composites (2002)

controlled

desired

p1

p2

p3

2 in

4 in

0 s

30 s

50 s

40 s

20 s

10 s

60 s1 2 3 4 5 6 7 8

so

lid

in

se

rt

0

50

100

150

200

250p

1

p2

p3

Inle

t P

ress

ure

[kP

a]

0

2

4

6

8

10

0 10 20 30 40 50 60

1,23,45,67,8

Time [sec]

Lo

cal P

erm

ea

bili

ty [

x10-9

m2 ]


Process Control with Real Time Numerical Simulations

Nielsen and Pitchumani, Composites Science and Technology (2002)

Numerical Flow

Simulator

Numerical Flow

Simulator

Flowrate Schedule

Set

Q(t) Y* (t+t)

desired flow scheme

Ydes(t+t)

q1 q2 q3

x

y on

line

flo

w s

enso

r

Flowrate InjectionHardware


Preform

Resin

Qopt(t)

Yact(t)

closed-loopprocess controller


Example Results

controlleddesired

q1

q2

q3

low

pe

rme

ab

ilit

yre

gio

n (

3x

4 i

n)

0 s

30 s

50 s

40 s

20 s

10 s

60 s

1 in

0

20

40

60

80

0 10 20 30 40 50 60

q1

q2

q3

Time [sec]

Flo

wra

te [

ml/m

in]

controlleddesired

q1

q2

q3

low

pe

rme

ab

ilit

yre

gio

n (

3x

8 i

n)

0 s

30 s

50 s

40 s

20 s

10 s

60 s

0

20

40

60

80

0 10 20 30 40 50 60

q1

q2

q3

Time [sec]

Flo

wra

te [

ml/m

in]


Active ControlJohnson and Pitchumani, Composites Science and Technology (2003); Composites A (2004);

Polymer Composites (2004)

Issue: Controllability of flow decreases away from the controlled injection ports in conventional injection based control

Active Control Concept: To locally alter (reduce) viscosity to compensate for low permeability areas in the preform

QuickTime™ and aTIFF (Uncompressed) decompressor



Active Control Example




Nondestructive Permeability Sensing: Concept

Carman-Kozeny model for permeability estimation

Simple and widely accepted

Permeability is defined as a function of hydraulic radius rf,

volume fraction f, and an empirical constant, the Kozeny

constant

Nondestructive permeability measurement

Principally sound attenuation is a function of geometry and fiber density so a relation should exist between permeability and attenuation

Fiber architectural terms (tortuosity and pore size) in the Carman-Kozeny equation are replaced by a function of the preform sound attenuation (), and mold cavity depth (b) 2

3

2* )1(

) (12

f

fNbCb


Permeability SensingMeasure preform attenuation to determine constants, C and N for different

preform architectures


Sources of Uncertainty in Materials Processing

Materials:Uncertainty in characterizing material properties such as viscosity, kinetic parameters,…

Operational: Uncertainty in parameter settings, monitoring/control

Design: Inaccuracies associated with description of process phenomena, property models etc.

Variable Product Quality

Interactive effects of uncertainty cause product quality variations


Stochastic Modeling Approach

Uncertain input parameters are quantified by probability distributions

Multiple process simulations are carried out for statistical samples selected

from the input distributions

Simulation outputs are characterized by suitable distribution functions and

by the mean and variance of the output distributions.

Deterministic model forms the basis for the stochastic framework

Sampler

Input parameters

Deterministic Model

Output parameter variabilities

Stochastic Model


Uncertainty Quantification and Robust Processing

Permeability, m2

0 200 400 600 800 1000 1200 1400

Freq

uen

cy

0.0

0.2

0.4

0.6

0.8

1.0

Permeabili ty, m2

0 200 400 600 800 1000 1200 1400 1600 1800

Freq

uen

cy

0.0

0.2

0.4

0.6

0.8

1.0

= 65 psig= 200 cps

f = 0.70

= 1.0= 0.28

= 40 psig= 200 cps

f = 0.70

= 1.30= 0.13

0.0/2.5

1.4E-02

2.8E-02

4.2E-02

5.6E-02

7.0E-02

200 cps

300 cps

400 cps

Knit, vf=0.62

0.0

5.0E-03

1.0E-02

1.5E-02

2.0E-02

280 320 360 400 440

Pressure, P [kPa]

Plain Weave, vf=0.65

Volume Fraction, vf

0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70

Pre

ssur

e, P

[kP

a]

300

325

350

375

400

425

Plain Weave

Knit

450

275

Plain Weave Exp.

Knit Exp.


Gaps and Opportunities

Models are not truly predictive yet eliminate fitting parameters focus on dealing with uncertainty and incorporating it in the modeling uncertainty quantification (kinetics, rheology, permeability, …) focus on microscale phenomena that have typically been empirically

treated Models should be computationally efficient for real-time applications

effective, physics-based, reduced order models or surrogate models for online control, stochastic analysis and optimization under uncertainty

parallel/distributed/agent-based computing paradigm Integrated materials design and processing framework to reduce overall

insertion time Sensors

need low cost, in-process/nonintrusive sensing (flow, cure, permeability,…)

reliability of sensor data Use of optimization techniques to improve process designs (for example,

better integrate flow and cure step) Active control schemes using physics-based models and incorporating

uncertainty Reduce human involvement in the processing (hand lay-up etc.) so as to

improve consistency of fabrication


Sensors, Control and AutomationSummary of Group Discussion

Common Theme Control based on models as a promising way Use of science-based models in real-time control applications Online parameter (permeability) determination Uncertainty in the parameters and need to control/design the

processes in the face of uncertainty Science-based approach to composites molding


GAPS/BARRIERS

Modeling Current models are not truly predictive yet! Better description of the micro and smaller scale phenomena to

reduce empiricism and to better predict the quality of parts. Models must account for uncertainty in the parameters (kinetics,

rheology, permeability,…) Materials characterization and uncertainty quantification Integrated models spanning the complete materials-processing-

microstructure-property/performance chain. Sensing and control fit in each of these links.

Validation and Verification Sensors

Maximize sensor use during development stages with a view to minimizing sensor use in production!

need low cost, in-process/nonintrusive sensing (flow, cure, permeability,…)

need greater reliability of sensor data Explore wireless sensing methods to reduce the complexity of the

control system Permeability sensor/scanner


GAPS/BARRIERS (contd.) Control

Pursue and develop active control schemes that better integrate the process models

Need standards and measures of quality for in process control and for part quality

Control for processing of emerging material systems such as nanocomposites

Computational/Integration Issues Commonality of database and data structures, naming convention,

etc., so that models and modules integrate seamlessly (Take a system level view and involving commercial software developers)

Models should be computationally efficient for real-time applications

– effective, physics-based, reduced order models or surrogate models for online control, stochastic analysis and optimization under uncertainty

– parallel/distributed/agent-based computing paradigm Automation

Reduce human involvement. Development of appropriate sensors and control is a right step in this direction

SYNERGY WITH OTHER GROUPS A natural synergy exists with the other groups. In particular the MM

and PM groups are closely interlinked with SCA. DO can contribute with efficient optimization methods. PP can provide some focus on what to sense/control


Flow Control: Controller ArchitectureNielsen and Pitchumani, Polymer Composites (2002)

Yact(t)

Fuzzy Logic-based


Fuzzy Logic-based


pro

cess con

troller

ANN-based flow simulator

SA-basedOptimizer

P(t) Y* (t+t)

desired flow scheme

Ydes(t+t)

p1 p2 p3

x

y on

line

flo

w s

enso

r

avg(t)



Preform

Resin

(t)

Popt(t)


Process Control with Real Time Numerical Simulations

Nielsen and Pitchumani, Composites Science and Technology (2002)

Numerical Flow

Simulator

Numerical Flow

Simulator

Flowrate Schedule

Set

Q(t) Y* (t+t)

desired flow scheme

Ydes(t+t)

q1 q2 q3

x

y on

line

flo

w s

enso

r



Preform

Resin

Qopt(t)

Yact(t)

closed-loopprocess controller


Stochastic Modeling Approach

Uncertain input parameters are quantified by probability distributions

Multiple process simulations are carried out for statistical samples selected

from the input distributions

Simulation outputs are characterized by suitable distribution functions and

by the mean and variance of the output distributions.

Deterministic model forms the basis for the stochastic framework

Sampler

Input parameters

Deterministic Model

Output parameter variabilities

Stochastic Model


Uncertainty Quantification and Robust Processing

Permeability, m2

0 200 400 600 800 1000 1200 1400

Freq

uen

cy

0.0

0.2

0.4

0.6

0.8

1.0

Permeabili ty, m2

0 200 400 600 800 1000 1200 1400 1600 1800

Freq

uen

cy

0.0

0.2

0.4

0.6

0.8

1.0

= 65 psig= 200 cps

f = 0.70

= 1.0= 0.28

= 40 psig= 200 cps

f = 0.70

= 1.30= 0.13

0.0/2.5

1.4E-02

2.8E-02

4.2E-02

5.6E-02

7.0E-02

200 cps

300 cps

400 cps

Knit, vf=0.62

0.0

5.0E-03

1.0E-02

1.5E-02

2.0E-02

280 320 360 400 440

Pressure, P [kPa]

Plain Weave, vf=0.65

Volume Fraction, vf

0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70

Pre

ssur

e, P

[kP

a]

300

325

350

375

400

425

Plain Weave

Knit

450

275

Plain Weave Exp.

Knit Exp.


Permeability SensingMeasure preform attenuation to determine constants, C and N for different

preform architectures

2

3

2* )1(

) (12

f

fNbCb

presentation summary: sensors, control and automation group

Documents

polymer composites

composites science

university of connecticutrole

controlactive control

flow progression

controllability of flow

future of modeling

prescribed volumetric