Simulation-based Design System for Flow Control in Liquid Composite Molding (LCM)

1. 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. GA/simulation-based design Final intuitive design Role of Flow Simulation in LCM Optimization [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. Darcy’s Law 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.

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

5. Implement the customized control action for Mode 29 Experimental resin arrival times t0, t1, t2, t3, t4 are all collected Disturbance Mode 29 is selected from the Database AG1 • Control action Mode 29 is taking place. • CS1 >>> Close IG2 • CS2 >>> Open AG1 • CS3 >>> Close IG1 • Vent Sensor >>> Close All Gates. CS2 IG1 IG2 CS3 CS1 AG2 Successful injection Case Study: Online Flow Monitoring & Strategic (On/Off) Injection Control TekscanTM Sensor Area (Pressure Grid Film) Initial injection gate (IG) with flow runner Fixed vent Auxiliary gate (AG) 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. Predicted flow front Actual flow front • ANN Simulator (Trained by Numerical Simulations) • SA Optimizer CCD Camera Q1 Q3 Q2 Line Sensor 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]. [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.

7. Electrical Resistance? Electrical Admittance? Time of Flight? Sensors Available for LCM Flow Monitoring • 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. 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 Server Raw Material Database Equipment Database Implementation of Process Monitoring and Control Process Simulations System Self-Improvement Quality Evaluation Composite Part Vision: Computer Controlled LCM System - Integration of Process Design, Automation, and Quality Control

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