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					Simulation-based Design System for
 Flow Control in Liquid Composite
         Molding (LCM)
                   Kuang-Ting Hsiao
           Department of Mechanical Engineering
               University of South Alabama




NSF/DOE/APC Workshop: Future of Modeling in Composites Molding Processes
                   June 9-10, 2004, Arlington, VA
             Role of Flow Simulation in LCM Optimization




   Final intuitive design                                         GA/simulation-based design




[1] K.T. Hsiao, M. Devillard, and S. G. Advani, “Simulation Based Flow Distribution Network Optimization for Vacuum Assisted
Resin Transfer Molding Process,” Modeling and Simulation in Materials Science and Engineering, 12(3), pp. S175-S190, 2004.
                     Flow Disturbance in LCM

Small variations on the local permeability and fiber volume fraction sometimes
make the filling pattern very different and cause unexpected dry spot!
Need reliable flow control to counteract the disturbance.
                                                            Darcy’s Law
                                                                    K
                                                          u            P
                                                                    
                                                                    
                                                                            1 K
                                                                   
                                                                        1  V f    P
                                                               r
                                                          ur
                        Design LCM Flow Control with
                    Simulation-based Liquid Injection Control
                                          Mesh         Resin Viscosity



                                         Preform Permeability
                                         Fiber Volume Fraction                      3. Optimally Place Sensors and Create
1. Gates/Vents Design[2].                                                           Database for Mold Filling Monitoring and
                                                                                    Permeability Characterization. [2,3]


                                                      SLIC


                                                        $$$
                                              Objective Function
  2. Layout of Flow Runners and
                                                & Constraints
                                                                              4. Optimally Place Auxiliary Gates and
  Flow Distribution Media. [1]                                                Create Mold Filling Control Strategies [2].


[2] K.T. Hsiao and S. G. Advani, “Flow sensing and control strategies to address race-tracking disturbances in resin transfer molding---
Part I: design and algorithm development,” Composites Part A: Applied Science and Manufacturing, (in press).
[3] M. Devillard, K.T. Hsiao, A. Gokce, and S. G. Advani, “On-line characterization of bulk permeability and race-tracking during the
filling stage in resin transfer molding process,” Journal of Composite Materials, 37(17), pp. 1525-1541, 2003.
                         Case Study: Online Flow Monitoring &
                          Strategic (On/Off) Injection Control
                                             TekscanTM Sensor Area
                                             (Pressure Grid Film)




                                                   Experimental resin                Disturbance Mode                Implement the
                                                        arrival times                29 is selected                  customized
                                                   t0, t1, t2, t3, t4 are all        from the Database               control action for
                                                          collected                                                  Mode 29
                                                         AG1                          Control action Mode 29 is taking place.
                                      IG1           CS2                           IG2 • CS1 >>> Close IG2
Initial injection gate (IG)                                        CS3
     with flow runner                                                                 • CS2 >>> Open AG1
                                                     CS1                              • CS3 >>> Close IG1
Fixed vent                                             AG2                            • Vent Sensor >>> Close All Gates.
Auxiliary gate (AG)
Disturbance detection sensor (DS)
                                                                                                          Successful injection
Control action trigger sensor (CS)

 [4] M. Devillard, K.T. Hsiao and S. G. Advani, “Flow sensing and control strategies to address race-tracking disturbances in resin
 transfer molding---Part II: automation and validation,” Composites Part A: Applied Science and Manufacturing (submitted).
                             Other Types of LCM Flow Control

Simulation-based Artificial Neural                                          Adaptive Control (Numerical
Network and Simulation-Annealing                                            Simulations may NOT be Necessary)
Control [5].                                                                [6].

                                Predicted flow front

                                  Actual flow front


                                   •ANN Simulator
                                   (Trained by
                                   Numerical
         CCD
                                   Simulations)
        Camera
                                   •SA Optimizer

   Q1           Q2           Q3


                                                                                            Line Sensor
[5] D. Nielsen, R. Pitchumani “Intelligent model-based control of preform permeation in liquid composite molding processes, with online
optimization”, Composites: Part A 32 (2001) 1789-1803.
[6] B. Minaie, W. Li, S. Jiang, K. Hsiao, R. Little “Adaptive Control of Non-Isothermal Filling in Resin Transfer Molding”, Proceedings of
49th International SAMPE Symposium and Exhibition, Long Beach, CA, May 16-20, 2004.
         Sensors Available for LCM Flow Monitoring

                               Electrical Resistance?
•DC point sensor
•SMART weave
•DC linear sensor
•Dielectric linear sensor              Electrical Admittance?
•Optic fiber sensor
•Electric time-domain reflectometry sensor
                                                         Time of Flight?
•CCD Camera
•Tekscan sensor (pressure grid film)

+
Interpretation algorithms to figure out the details of LCM flow from the
limited (point, linear, 2-D) sensor feedback.
                             Future Needs

1.  Reduce mold tooling/equipment cost using modular approach.
2.  Reduce the process development time and cost by minimizing the use of
    trial-and-error.
3. Enhance the capability of manufacturing large, complex, and net-shaped
    part.
4. Reduce the cycle time by optimally merging the mold filling stage and cure
    stage.
5. Need to gain better process controllability against disturbance during
    process.
6. Need complete and rigorous heat transfer models for non-isothermal LCM
    simulation.
7. Include dimension tolerance modeling into LCM design.
8. Need a systematic approach to tie the final part quality with processing
    control.
9. Need reliable sensors and interpretation algorithms.
10. Reduce the portion of human factor in LCM operation.
Vision: Computer Controlled LCM System - Integration
  of Process Design, Automation, and Quality Control

                                                             Fiber Preform         Resin

How do we formulate the building blocks and connect them by exploiting the
knowledge of composites manufacturing, information technology and robotics?

        Database for Past Processes                  LCM Process
                                                    Design/Analysis
        Raw Material Database                           Server

          Equipment Database

                                                          Implementation of Process
                                                           Monitoring and Control
        Process Simulations

                          System Self-Improvement
                                                              Quality Evaluation


                                                               Composite Part
      Challenges of the Future Integrated LCM System

System Reliability                    Process Simulation
•Sensor and Sensing Algorithm         •Non-isothermal Molding
•Control Algorithm                    •3-D Simulation
•Controllability                      •Preform Deformation in LCM
•Algorithm/Methodology to Integrate   •Micro-Voids Formation/Migration
the Design, Automation, and Quality   •Residual Stress/Strain
Control
•Self-Improving Algorithm
•Operation Repeatability
                                      Performance Evaluation
                                      •Influence of Defects
Process Physics                       •Influence of Residual Stress/Strain
•New Resins                           •Influence of Other Processing Parameters
•New Fillers                          such as Pressure, Cure Cycle, Moisture
•New Fiber/Fabric Systems             Content, Mold Tools, etc.

				
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posted:5/1/2011
language:English
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