David Nielsen Defense

MODEL-BASED CONTROL STRATEGIES

FOR FLOW IN RESIN TRANSFER

MOLDING OF COMPOSITE MATERIALS

David Nielsen

Composites Processing Laboratory

Department of Mechanical Engineering

University of Connecticut

Storrs, Connecticut

Composites Processing Laboratory, University of Connecticut


Outline

Introduction

Numerical Flow Model

Flow Control Strategies

Intelligent Model-Based Flow Control with Online Optimization

Intelligent Control with Real-Time Preform Permeability Estimation

Control Using Real-time Numerical Process Simulations

Conclusions


Introduction

Preform permeation is a critical step

run-to-run variabilities

voids and dry spots = part quality

RTM Control Schemes (literature search)

empirically trained neural networks (Demirci and Coulter, 1995)

traditional PI control (Lee and Rice, 1998)

offline numerical simulations (Kang, et al.,2000)

Benefits of online model predictive control

incorporates process physics; is robust and effective

however, needs rapid model prediction (real-time)

Preforming Preform

PermeationCuring Composite

Part

need for control


Numerical Flow Model Formulation and Solution

resin

air

0.0

.9 1.0 1.0 1.0

1.01.0

0.80.70.2

0.0

0.5

x

y

u

v

mold cavity ()

y

pv

x

pu

yx

,

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)

Numerical Model of viscous fluid

flow through a Hele-Shaw Cell

(Guan and Pitchumani, 2000)


Validation of the Numerical Flow Model (64 x 64)

numerical model

actual

q1=10 q

2=20 q

3=50 ml/min


Outline

Introduction






Conclusions


Controller Architecture

Ydes(t+t)

Yact(t)

RTM

ANN-based

process

simulator

SA-based

optimizer

process controller

q1 q2 q3

Q(t)Y* (t+t)


Artificial Neural Network-based Process Simulator

Analogous to the human brain; able to model a system’s inputs/outputs

neurons interconnected by synapse to form a network

3300 input/output patterns from Num. Flow Model

ANN-based Process Simulator:

11 inputs (8 flow front, 3 flowrate)

8 output (8 flow front progressions)

10 neurons in a hidden layer

neural

network

outputs

neural

network

inputs

input layerhidden layer

output layer

ƒact

b

summation activation

bias


Simulated Annealing-Based Online Optimizer

Tem

pera

ture

number of steps

high probability

of simplex

reconfiguration

low probability

of simplex

reconfiguration

primary vertex

global solution

walks

walks

the

simplex

primary vertex

potential

primary

vertex

reflection expansion contraction

Analogous to atomically rearranging a

substance into a highly ordered

crystalline structure by way of slowly

cooling minimizing the energy state

A decrease in energy is always

accepted.

An increase in energy is accepted

with a probability that decreases

within a temperature schedule.

Probability of rearrangement,

given by Metropolis Criterion

4-dimensional simplex

T

E

BeP


Experimental Implementation

Motor

Controllers

D/A board

Intelligent

Controller

Architecture

in LabVIEW

Frame

Grabber

CCD

Camera

8”8”

inlets

vents

pumps

mold

materials:

• glycerin (similar viscosity to EPON 815C/EPICURE 8274)

• Owens-Corning continuous strand mat


0

25

50

75

0 12 24 36 48 60

q1

q2

q3

Time [sec]

Flo

wra

te [

ml/m

in]

desired

controlled

q1

q2

q3

Uniform Fill Control


Symmetric Fill Control

0

25

50

75

0 26 52 78 104 130

q1

q2

q3

Time [sec]

Flo

wra

te [

ml/m

in]

desired

controlled

q1

q2

q3


Uniform Fill Control (movie)


Asymmetric Fill Control (movie)


Outline

Introduction






Conclusions

Nielsen D.R., Pitchumani R., 2001a, ``Intelligent Model-based Control of Preform

Permeation in Liquid Composite Molding Processes with Online Optimization," In

Press: Composites Part A: Applied Science and Manufacturing.

Nielsen D.R., Pitchumani R., 2000a, ``Real Time Model-Predictive Control of Preform

Permeation in Liquid Composite Molding Processes," Proceedings of NHTC, ASME

National Heat Transfer Conference, Pittsburgh, Pennsylvania, USA.



Yact(t)

Fuzzy Logic-

based

Permeability

Estimator

pro

cess c

on

trolle

r

ANN-based flow

simulator

SA-based

Optimizer

P(t) Y* (t+t)

desired flow

scheme

Ydes(t+t)

p1 p2 p3

x

y on

lin

e f

low

se

nso

r

avg(t)

Pressure Injection

Hardware

Preform

Resin

(t)

Popt(t)

y

pv

y


Fuzzy Modeling (Babuska, 1998)

EXAMPLE:

IF it the temperature is low (SMALL)

THEN the heating rate is high

IF the temperature is medium (MEDIUM)

THEN the heating rate is normal

IF the temperature is high (LARGE)

THEN the heating rate is low

TRAINING:

•The fuzzy clustering technique is used for

rule extraction of the fuzzy model

•Clusters similar objects to a prototypical

object using a Euclidean distance of measure

•Each cluster becomes a single rule:

IF x is seti

THEN y = fi(x)

•No defuzzification in this model

REF: Babuska, R., 1998, Fuzzy Modeling for

Control, Kluwer Academic Publishers,

Boston.

HEATING RATE

(crisp outputs)

Mem

bers

hip

SM

AL

L

ME

DIU

M

LA

RG

E

0.2

0.5

Fuzzification1.

0

0TEMPERATURE

(crisp inputs)

low

5ºC 30ºC

Decision

table

Model

Coefficients

21ºC

MEDIUM (0.5)

LARGE (0.2)


Experimental Implementation

pressure

controllers

vents

inlets

injection

guns

mold

air supply

D/A

board

Intelligent

Controller

Architecture

in LabVIEW

Frame

Grabber

CCD

Camera


Uniform Fill Control - High Permeability Preform

0

50

100

150

200

250

p1

p2

p3

Inle

t P

ressu

re [

kP

a]

0

2

4

6

8

10

0 10 20 30 40 50 60

entire region

Time [sec]

Pe

rme

ab

ility

[x1

0-9

m2]

controlled

desired

p1

p2

p3

0 s

30 s

50 s

40 s

20 s

10 s

60 s1 2 3 4 5 6 7 8


0

50

100

150

200

250

p1

p2

p3

Inle

t P

ressu

re [

kP

a]

0

2

4

6

8

10

0 20 40 60 80 100

entire region

Time [sec]

Pe

rme

ab

ility

[x1

0-9

m2]

controlled

desired

p1

p2

p3

0 s

45 s

75 s

60 s

30 s

15 s

100 s1 2 3 4 5 6 7 8

90 s

Uniform Fill Control - Low Permeability Preform


0

50

100

150

200

250

p1

p2

p3

Inle

t P

ressu

re [

kP

a]

0

2

4

6

8

10

0 10 20 30 40 50 60

low permeability region

high permeability region

Time [sec]

Pe

rme

ab

ility

[x1

0-9

m2]

controlled

desired

low

pe

rme

ab

ilit

y

reg

ion

(2

.5x

8 i

n)

hig

h p

erm

ea

bil

ity

reg

ion

(5

.5x

8 i

n)

p1

p2

p3

0 s

30 s

50 s

40 s

20 s

10 s

60 s1 2 3 4 5 6 7 8

Uniform Fill Control - Low Permeability Side Region


Uniform Fill Control - Complex Shaped Mold

0

50

100

150

200

250

p1

p2

p3

Inle

t P

ressu

re [

kP

a]

0

2

4

6

8

10

0 10 20 30 40 50 60

1,2

3,4

5,6

7,8

Time [sec]

Pe

rme

ab

ility

[x1

0-9

m2]

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

sert


Uniform Fill Control (movie)


Outline

Introduction






Conclusions

Nielsen D.R., Pitchumani R., 2001b, ``Control of Flow in Resin Transfer Molding with

Real-time Preform Permeability Estimation," Submitted to: Polymer Composites.

Nielsen D.R., Pitchumani R., 2000b, ``Neural Network Based Control of Preform

Permeation in Resin Transfer Molding Processes with Real-Time Permeability

Estimation," Proceedings of IMECE, ASME International Mechanical Engineering

Congress and Exposition, Orlando, Florida, USA.



Numerical

Flow

Simulator

Flowrate

Schedule

Set

Q(t) Y* (t+t)

desired flow

scheme

Ydes(t+t)

q1 q2 q3

x

y on

lin

e f

low

sen

so

r

Flowrate Injection

Hardware

Preform

Resin

Qopt(t)

Yact(t)

closed-loop

process controller


64x64

13x13

q1

q2

q3

64x64

13x13

q1

q2

q3

64x64

13x13

q1

q2

q3

64x64

13x13

q1

q2

q3

Figure 0

Validation of the Coarse Mesh Numerical Model

•Simulation on coarse mesh

was found to be accurate

because of close match to

previous fine mesh simulations

• A single coarse mesh

simulation was found to

execute within a time much

faster then the process itself.

• simulation was used for real-

time control.

[64x64] [13x13]


Trail Flowrate Schedule Set

t = control interval [s] = 2s

t* = schedule execution

time 1.5s (constraint)

tmax = maximum schedule

execution time = 1.5s

1 0 0 0

2 0 40 0

3 0 80 0

4 40 0 0

5 80 0 0

6 0 0 40

7 0 0 80

8 40 40 40

9 80 80 80

10 40 0 40

11 80 0 80

12 40 40 0

13 80 80 0

14 0 40 40

15 0 80 80

16 40 0 80

17 80 0 40

18 40 80 0

19 80 40 0

20 0 40 80

21 0 80 40

22 40 80 40

23 80 40 80

24 40 40 80

25 80 40 40

26 80 80 40

27 40 80 80

zero solution

single inlet port

solutions

equal valued solutions

two inlet port on

solutions

three inlet port on

solutions

trial

solution

number q1 q2 q3

t* tmax

best flowrates sent

to online hardware


Decelerating Fill Control

0

20

40

60

80

0 20 40 60 80 100

q1

q2

q3

Time [sec]

Flo

wra

te [

ml/m

in]

controlled

desired

q1

q2

q3

0 s

30 s

50 s

40 s

20 s

10 s

60 s

70 s

80 s

90 s


0

20

40

60

80

0 10 20 30 40 50 60 70 80

q1

q2

q3

Time [sec]

Flo

wra

te [

ml/m

in]

controlled

desired

q1

q2

q3

0 s

50 s

70 s

60 s

20 s

10 s

80 s

30

Uniform Fill Control with Flow Front Halt


Asymmetric Fill Control

0

20

40

60

80

0 20 40 60 80 100 120 140

q1

q2

q3

Time [sec]

Flo

wra

te [

ml/m

in]

controlled

desired

q1

q2

q3

106 s

122 s

0 s

58 s

74 s

26 s

10 s

42 s

90 s


Uniform Fill Control with Racetracking Effects

0

20

40

60

80

0 10 20 30 40 50 60

q1

q2

q3

Time [sec]

Flo

wra

te [

ml/m

in]

controlled

desired

q1

q2

q3

ind

uc

ed

ra

ce

tra

ck

ing

0 s

30 s

50 s

40 s

20 s

10 s

60 s

Nielsen D.R., Pitchumani R., 2001c, ``Closed-Loop Flow Control in Resin Transfer Molding

using Real-time Numerical Process Simulations," Submitted to: Composites Science and

Technology.


Conclusions

Three different control strategies were presented and

implemented for real-time control of RTM.

A systematic study on the use of an intelligent model-based

optimal control for RTM was presented.

Local preform permeability values were determined using fuzzy

logic and used as inputs to the process model for control.

The feasibility of implementing finite difference numerical

process simulations in real-time control was demonstrated.

All controllers where implemented in LabVIEW for on-line

process control and may be readily realized for an actual process

All the controllers were shown to be able to steer fluid through

homogeneous, heterogeneous, racetracking, and complex

geometry scenarios alike.


Project Funded by the Office of Naval ResearchContract No. N00014–97–1–0730James J. Kelly, Scientific Officer

Advisory Committee:Ranga Pitchumani

Luke AchenieKazem Kazerounian

Wilson Chiu

Documents

David Nielsen Defense