1. Department of Mechanical Engineering University of South Alabama
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

2. Role of Flow Simulation in LCM Optimization
GA/simulation-based design
Final intuitive 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.

3. 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

4. Design LCM Flow Control with Simulation-based Liquid Injection Control
Preform Permeability
Fiber Volume Fraction
Mesh
Resin Viscosity
3. Optimally Place Sensors and Create Database for Mold Filling Monitoring and Permeability Characterization. [2,3]
1. Gates/Vents Design[2].
SLIC
2. Layout of Flow Runners and Flow Distribution Media. [1]
4. Optimally Place Auxiliary Gates and Create Mold Filling Control Strategies [2].
$$$
Objective Function
& Constraints
[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 , 2003.

5. Case Study: Online Flow Monitoring & Strategic (On/Off) Injection Control
TekscanTM Sensor Area
(Pressure Grid Film)
Experimental resin arrival times
t0, t1, t2, t3, t4 are all collected
Disturbance Mode 29 is selected from the Database
Implement the customized control action for Mode 29
Control action Mode 29 is taking place.
CS1 >>> Close IG2
CS2 >>> Open AG1
CS3 >>> Close IG1
Vent Sensor >>> Close All Gates.
AG1
AG2
CS2
IG1
IG2
CS1
CS3
Initial injection gate (IG) with flow runner
Fixed vent
Auxiliary gate (AG)
Successful injection
Disturbance detection sensor (DS)
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).

6. Other Types of LCM Flow Control
Simulation-based Artificial Neural Network and Simulation-Annealing Control [5].
Adaptive Control (Numerical Simulations may NOT be Necessary) [6].
Line Sensor
CCD
Camera
ANN Simulator (Trained by Numerical Simulations)
SA Optimizer Q1 Q2 Q3
Actual flow front
Predicted flow front
[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)
[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.

7. Sensors Available for LCM Flow Monitoring
Electrical Resistance?
Electrical Admittance?
Time of Flight?
DC point sensor
SMART weave
DC linear sensor
Dielectric linear sensor
Optic fiber sensor
Electric time-domain reflectometry sensor
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.

8. Future Needs
Reduce mold tooling/equipment cost using modular approach.
Reduce the process development time and cost by minimizing the use of trial-and-error.
Enhance the capability of manufacturing large, complex, and net-shaped part.
Reduce the cycle time by optimally merging the mold filling stage and cure stage.
Need to gain better process controllability against disturbance during process.
Need complete and rigorous heat transfer models for non-isothermal LCM simulation.
Include dimension tolerance modeling into LCM design.
Need a systematic approach to tie the final part quality with processing control.
Need reliable sensors and interpretation algorithms.
Reduce the portion of human factor in LCM operation.

9. Vision: Computer Controlled LCM System - Integration of Process Design, Automation, and Quality Control
Fiber Preform
Raw Material Database
Equipment Database
LCM Process Design/Analysis Server
Implementation of Process Monitoring and Control
Composite Part
Quality Evaluation
Database for Past Processes
Process Simulations
Resin
How do we formulate the building blocks and connect them by exploiting the knowledge of composites manufacturing, information technology and robotics?
System Self-Improvement

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