Kuang-Ting Hsiao

Department of Mechanical Engineering

University of South Alabama

Simulation-based Design System for Flow Control in Liquid Composite

Molding (LCM)

NSF/DOE/APC Workshop: Future of Modeling in Composites Molding ProcessesJune 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.

PV

u

Pu

f

r

r

K

K

1

1

Darcy’s Law

Design LCM Flow Control with Simulation-based Liquid Injection Control

2. Layout of Flow Runners and Flow Distribution Media. [1]

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

4. Optimally Place Auxiliary Gates and Create Mold Filling Control Strategies [2].

Preform PermeabilityFiber Volume Fraction

Mesh Resin Viscosity

SLICSLIC

$$$$$$

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. 1525-1541, 2003.

Case Study: Online Flow Monitoring & Strategic (On/Off) Injection Control

Control action trigger sensor (CS)

Initial injection gate (IG) with flow runner

Auxiliary gate (AG)

Fixed vent

Disturbance detection sensor (DS)

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

TekscanTM Sensor Area(Pressure Grid Film)

Control action Mode 29 is taking place. • CS1 >>> Close IG2• CS2 >>> Open AG1• CS3 >>> Close IG1• Vent Sensor >>> Close All Gates.

AG1

AG2

CS2IG1 IG2

CS1

CS3

Successful injection

[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

Adaptive Control (Numerical Simulations may NOT be Necessary) [6].

Simulation-based Artificial Neural Network and Simulation-Annealing Control [5].

Line Sensor

CCD Camera

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

Q1 Q2Q3

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) 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.

•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)

Sensors Available for LCM Flow Monitoring

Electrical Resistance?

Electrical Admittance?

Time of Flight?

+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

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

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

Process Physics•New Resins•New Fillers•New Fiber/Fabric Systems

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.