Presentation Summary: Sensors, Control and Automation Group

1. Presentation Summary:Sensors, Control and Automation Group NSF/DOE/APC Workshop: The Future of Modeling in Composites Molding Processes June 9-10, 2004

2. Role of Modeling in Bridging the Science and Practice of Composites Processing Ranga Pitchumani Composites Processing Laboratory Department of Mechanical Engineering University of Connecticut Storrs, Connecticut 06269-3139 http://www.engr.uconn.edu/cml NSF/DOE/APC Workshop: Future of Modeling in Composites Molding Processes Sensing, Control and Automation Session Arlington, VA • June 9–10, 2004

3. Agenda Items • Introduction • Presentations (9:00–10:30am) • Pitchumani • Coulter • Griffith • Glancey • Hsiao • Kennedy • Discussion and Summary Preparation (10:30–11:45am)

4. Outline • Control for Mold Filling in Liquid Molding Processes • Permeability Sensing • Analysis and Design of Processes under Uncertainty

5. Preforming Preform Permeation • 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) Composite Product Curing need for control Liquid Composite Molding Processes

6. 0.0 0.0 0.2 air u v 0.8 0.5 0.7 1.0 1.0 1.0 resin mold cavity () y x Flow Modeling Flow velocity through Darcy’s law Continuity Eqn: Flow progression obtained using a volume tracking method 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)

7. online flow sensor y x Flow Control: Controller ArchitectureNielsen and Pitchumani, Polymer Composites (2002) ANN-based flow simulator Yact(t) Preform (t) Fuzzy Logic-based Permeability Estimator process controller P(t) Y*(t+t) Resin p1 p2 p3 avg(t) SA-based Optimizer Pressure Injection Hardware Popt(t) desired flow scheme Ydes(t+t)

8. Controller Implementation CCD Camera Frame Grabber air supply Pressure controllers D/A board injection guns Controller Architecture in LabVIEW inlets mold vents

9. Real Time Control Movie

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

11. Example Controlled RunNielsen and Pitchumani, Polymer Composites (2002)

12. closed-loop process controller Preform Numerical Flow Simulator Yact(t) online flow sensor Resin Q(t) Y*(t+t) q1 q2 q3 Flowrate Schedule Set desired flow scheme Ydes(t+t) Flowrate Injection Hardware Qopt(t) y x Process Control with Real Time Numerical SimulationsNielsen and Pitchumani, Composites Science and Technology (2002)

13. Example Results

14. 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

15. Active Control Example

16. Active Control Example

17. 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 nf, and an empirical constant, the Kozeny constant K • 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 (a), and mold cavity depth (b)

18. Permeability Sensing Measure preform attenuation to determine constants, C and N for different preform architectures

19. Materials: Uncertainty in characterizing material properties such as viscosity, kinetic parameters,… Design: Inaccuracies associated with description of process phenomena, property models etc. Uncertainty in parameter settings, monitoring/control Operational: Variable Product Quality Sources of Uncertainty in Materials Processing Interactive effects of uncertainty cause product quality variations

20. Stochastic Modeling Approach Stochastic Model Deterministic Model Sampler Output parameter variabilities Input parameters • 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

21. 450 425 400 375 Knit Pressure, P [kPa] 350 325 300 Plain Weave Knit Exp. 275 Plain Weave Exp. 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 Volume Fraction, vf Uncertainty Quantification and Robust Processing

22. 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

23. 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

24. 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

25. 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

26. online flow sensor y x Flow Control: Controller ArchitectureNielsen and Pitchumani, Polymer Composites (2002) ANN-based flow simulator Yact(t) Preform (t) Fuzzy Logic-based Permeability Estimator process controller P(t) Y*(t+t) Resin p1 p2 p3 avg(t) SA-based Optimizer Pressure Injection Hardware Popt(t) desired flow scheme Ydes(t+t)

27. closed-loop process controller Preform Numerical Flow Simulator Yact(t) online flow sensor Resin Q(t) Y*(t+t) q1 q2 q3 Flowrate Schedule Set desired flow scheme Ydes(t+t) Flowrate Injection Hardware Qopt(t) y x Process Control with Real Time Numerical SimulationsNielsen and Pitchumani, Composites Science and Technology (2002)

28. Stochastic Modeling Approach Stochastic Model Deterministic Model Sampler Output parameter variabilities Input parameters • 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

29. 450 425 400 375 Knit Pressure, P [kPa] 350 325 300 Plain Weave Knit Exp. 275 Plain Weave Exp. 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 Volume Fraction, vf Uncertainty Quantification and Robust Processing

30. Permeability Sensing Measure preform attenuation to determine constants, C and N for different preform architectures