NSF/DOE/APC Future Modeling in Composites Molding Processes Workshop John P. Coulter Professor and Associate Dean P.C. Rossin College of Engineering and Applied Science Lehigh University Bethlehem, Pennsylvania 18015

Research Activities Related to Flow Processes During Composite Manufacturing

Materials & Measurements Processing & Manufacturing Sensors and Controls Properties & Performance Design & Optimization Flow Sensing with embedded distributed electronic sensors for Neural network control of filling Neural network curing control Grenestedt Group Design and testing of Large VARTM produced sandwich structures Monotonic, Fatigue & Fracture Studies of Polymeric Systems Study of Molecular Orientation During Melt-Processing Mechanical Properties Enhancement using VAIM Modeling Underfill Resin Cure

Melt Manipulation During Molding Processes Birefringence Observation – Polycarbonate Conventional VAIM

Flow Control During Molding Processes With the capability to control melt flow to portions of the mold, enhanced weldline placement can now be realized. This is shown to the left, where a weldline was controllably moved to various locations within the final product. Weldline Positioning Weldline positioning within PC test samples Successful family molding Dual Gate Valve 1 Valve 2 Dual Gate SEC Part 1 Sprue SEC Part 2 Schematic diagram of custom rotary valve implementation Trapezoidal Runner

Conveyer Belt Underfill Cure Modeling in a Chip Scale Package Solder Bump Underfill t = 3 min Incomplete Cure t = 2 mint = 1 min t = 4 min b c Complete Cure Case 2: Preheat Bumps Prior to Resin Underfill Case 1. Hypothetical Manufacturing Process

Sensors, Control and Automation Cavity Pressure Based Product Quality Determination Embedded Electronic Sensors for the Monitoring of Impregnation Processes Science Based Neural Network Control of Impregnation Processes Neural Network Control of Autoclave Cure

“Inverse” Neural Network Structure “Forward” Neural Network Structure

Vision For the Future of Composites Manufacturing: Intelligent Science-Based Processing

Process Modification Subsystems Product Quality Sensing Subsystems Intelligent Manufacturing Science Research: Perceived Gaps Production on Target Machines Optimal Control Subsystems Appropriate Integration on a Common Platform

Possible Future Research Thrusts: Materials rheology studies with target processing conditions Science-based material flow modeling Enhanced process and product quality monitoring during processing Enhanced process adaptation and control Processing of nano-composite systems