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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

Composites Processing Laboratory, University of Connecticut


Introduction

Preforming

Preform Permeation

Curing

Composite Part

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

need for control

 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)

Composites Processing Laboratory, University of Connecticut


Numerical Flow Model Formulation and Solution Flow velocity through Darcy’s law

 y p  x p u ,v    x  y

0.0

0.0

air 0.2

Numerical Model of viscous fluid flow through a Hele-Shaw Cell (Guan and Pitchumani, 2000)

u

0.5

0.7

0.8

1.0

1.0

1.0

v .9

resin Boundary Conditions:

1.0

1.0

inlet ports = at a prescribed volumetric flowrate or at a prescribed pressure exit vents = each at atmospheric pressure mold walls = zero volumetric flowrate (impenetrable)

mold cavity ()

y

x

Composites Processing Laboratory, University of Connecticut


Validation of the Numerical Flow Model (64 x 64) numerical model actual

q =10 1

q =20 2

q =50 ml/min 3

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

Composites Processing Laboratory, University of Connecticut


Controller Architecture process controller Yact(t) RTM ANN-based process simulator Q(t) q1

q2

Y* (t+ď „t)

q3

SA-based optimizer

Ydes(t+ď „t)

Composites Processing Laboratory, University of Connecticut


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)

summation activation

ƒact

 10 neurons in a hidden layer

b neural network inputs

bias

neural network outputs

output layer hidden layer input layer Composites Processing Laboratory, University of Connecticut


Simulated Annealing-Based Online Optimizer primary vertex

Analogous to atomically rearranging a substance into a highly ordered crystalline structure by way of slowly cooling minimizing the energy state

the simplex reflection

expansion

contraction

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 E  BT

primary vertex

Temperature

potential primary vertex

high probability of simplex reconfiguration walks

walks

Pe

low probability of simplex reconfiguration global solution

4-dimensional simplex

number of steps Composites Processing Laboratory, University of Connecticut


Experimental Implementation Frame Grabber

Motor Controllers

CCD Camera

pumps Intelligent Controller Architecture in LabVIEW

inlets mold

D/A board

8”

8” vents

materials: • glycerin (similar viscosity to EPON 815C/EPICURE 8274) • Owens-Corning continuous strand mat Composites Processing Laboratory, University of Connecticut


Uniform Fill Control 75

Flowrate [ml/min]

desired controlled

50

25 q1 q2 q

3

0 0

12

24

36

48

60

Time [sec]

q

1

q

2

q

3

Composites Processing Laboratory, University of Connecticut


Symmetric Fill Control 75 desired controlled

q1 q

Flowrate [ml/min]

2

q

3

50

25

0 0

26

52

78

104

130

Time [sec]

q

1

q

2

q

3

Composites Processing Laboratory, University of Connecticut


Uniform Fill Control (movie)

Composites Processing Laboratory, University of Connecticut


Asymmetric Fill Control (movie)

Composites Processing Laboratory, University of Connecticut


Outline  Introduction  Numerical Flow Model  Flow Control Strategies Intelligent Model-Based Flow Control with Online Optimization 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. 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.

Intelligent Control with Real-Time Preform Permeability Estimation

Control Using Real-time Numerical Process Simulations

 Conclusions

Composites Processing Laboratory, University of Connecticut


Controller Architecture

Preform

Resin y p1 x

p2

Pressure Injection Hardware

p3

Yact(t)

(t)

Fuzzy Logicbased Permeability Estimator

Y* (t+t)

P(t)

avg(t)

SA-based Optimizer

process controller

online flow sensor

ANN-based flow simulator

Popt(t)

 y p v  y

desired flow scheme

Ydes(t+t)

Composites Processing Laboratory, University of Connecticut


Fuzzy Modeling (Babuska, 1998) Fuzzification

TRAINING: •The fuzzy clustering technique is used for rule extraction of the fuzzy model

MEDIUM

0.5

LARGE

SMALL

Membership

1. 0

0.2

TEMPERATURE (crisp inputs)

0 5ºC

21ºC

30ºC

MEDIUM (0.5) LARGE (0.2)

Decision table

•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.

EXAMPLE: IF it the temperature is low (SMALL) THEN the heating rate is high

Model Coefficients

HEATING RATE (crisp outputs)

•Clusters similar objects to a prototypical object using a Euclidean distance of measure

low

IF the temperature is medium (MEDIUM) THEN the heating rate is normal IF the temperature is high (LARGE) THEN the heating rate is low

Composites Processing Laboratory, University of Connecticut


Experimental Implementation Frame Grabber

air supply

CCD Camera

pressure controllers D/A board

Intelligent Controller Architecture in LabVIEW

injection guns

mold

inlets

vents Composites Processing Laboratory, University of Connecticut


Uniform Fill Control - High Permeability Preform 1

2

3

4

5

6

7

8

250

60 s

p

1

Inlet Pressure [kPa]

50 s

40 s

30 s

p2

200

p3 150

100

50

20 s

10 0

0s

p

1

p

2

p

3

2

8

-9

controlled desired

Permeability [x10 m ]

10 s

6

4

2 entire region 0 0

10

20

30

40

50

60

Time [sec]

Composites Processing Laboratory, University of Connecticut


Uniform Fill Control - Low Permeability Preform 1

2

3

4

5

6

7

8

250

100 s

p1

Inlet Pressure [kPa]

90 s

75 s

60 s

45 s

p

200

2

p

3

150

100

50

30 s

10 0 entire region

p

1

2

8

-9

controlled desired

0s

Permeability [x10 m ]

15 s

p

2

p

3

6

4

2

0 0

20

40

60

80

100

Time [sec]

Composites Processing Laboratory, University of Connecticut


Uniform Fill Control - Low Permeability Side Region 1

2

3

4

5

6

7

8

250

60 s

p

1

20 s

high permeability region (5.5x8 in)

30 s

low permeability region (2.5x8 in)

40 s

Inlet Pressure [kPa]

50 s

p

200

2

p3 150

100

50

100

0s

p

1

p

2

p

3

2

8

-9

controlled desired

Permeability [x10 m ]

10 s

6

4

2 low permeability region high permeability region 0 0

10

20

30

40

50

60

Time [sec]

Composites Processing Laboratory, University of Connecticut


Uniform Fill Control - Complex Shaped Mold 1

2

3

4

5

6

7

8

250

60 s

p 50 s

40 s

Inlet Pressure [kPa]

solid insert

1

30 s

p2

200

p3 150

100

50

20 s

10 0

4 in

1,2 3,4 5,6 7,8

p

1

2

2 in

8

-9

0s

controlled desired

Permeability [x10 m ]

10 s

p

2

p

3

6

4

2

0 0

10

20

30

40

50

60

Time [sec]

Composites Processing Laboratory, University of Connecticut


Uniform Fill Control (movie)

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 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. Nielsen D.R., Pitchumani R., 2001b, ``Control of Flow in Resin Transfer Molding with Real-time Preform Permeability Estimation," Submitted to: Polymer Composites.

Control Using Real-time Numerical Process Simulations

 Conclusions

Composites Processing Laboratory, University of Connecticut


online flow sensor

Controller Architecture

Preform

Resin y q1

x

q2

Flowrate Injection Hardware

closed-loop process controller

Numerical Flow Simulator

Yact(t)

Y* (t+ď „t)

Q(t)

q3 Qopt(t)

Flowrate Schedule Set

desired flow scheme

Ydes(t+ď „t)

Composites Processing Laboratory, University of Connecticut


Validation of the Coarse Mesh Numerical Model 64x64 13x13

[64x64]

64x64 13x13

[13x13]

•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.

q

1

q

2

q

3

q

1

q

2

q

3

64x64 13x13

• simulation was used for realtime control.

64x64 13x13

q

1

q

2

q

3

q

1

q

2

q

3

Composites Processing Laboratory, University of Connecticut


Trail Flowrate Schedule Set

t = control interval [s] = 2s t* = schedule execution time  1.5s (constraint) tmax = maximum schedule execution time = 1.5s

best flowrates sent to online hardware

t*  tmax

trial solution number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

q1

q2 0 0 0 40 80 0 0 40 80 40 80 40 80 0 0 40 80 40 80 0 0 40 80 40 80 80 40

q3 0 40 80 0 0 0 0 40 80 0 0 40 80 40 80 0 0 80 40 40 80 80 40 40 40 80 80

0 0 0 0 0 40 80 40 80 40 80 0 0 40 80 80 40 0 0 80 40 40 80 80 40 40 80

zero solution single inlet port solutions

equal valued solutions

two inlet port on solutions

three inlet port on solutions

Composites Processing Laboratory, University of Connecticut


Decelerating Fill Control 80

90 s 80 s

q1

70 s

Flowrate [ml/min]

60 s 50 s 40 s 30 s

q2

60

q

3

40

20

20 s

0 0

10 s

20

40

60

80

100

Time [sec] controlled desired

0s

q

1

q

2

q

3

Composites Processing Laboratory, University of Connecticut


Uniform Fill Control with Flow Front Halt 80

80 s

controlled desired

q1 q

2

Flowrate [ml/min]

70 s

60 s

30 50 s

60

q

3

40

20

20 s

0 0 10 s

10

20

30

40

50

60

70

80

Time [sec]

0s

q

1

q

2

q

3

Composites Processing Laboratory, University of Connecticut


Asymmetric Fill Control 80 controlled desired

90 s

Flowrate [ml/min]

122 s

74 s

106 s

58 s 42 s

26 s

60

40

q1

20

q

2

q

3

0

10 s

0

20

40

60

80

100

120

140

Time [sec] 0s

q

1

q

2

q

3

Composites Processing Laboratory, University of Connecticut


Uniform Fill Control with Racetracking Effects 80

60 s

controlled desired

40 s

30 s

20 s

Flowrate [ml/min]

induced racetracking

50 s

60

40

q

1

20

q2 q3

0 0

10 s

10

20

30

40

50

60

Time [sec]

0s

q

1

q

2

q

3

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.

Composites Processing Laboratory, University of Connecticut


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.

Composites Processing Laboratory, University of Connecticut


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

Advisory Committee: Ranga Pitchumani Luke Achenie Kazem Kazerounian Wilson Chiu

Composites Processing Laboratory, University of Connecticut

David Nielsen Defense  

D Nielsen Defense

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