NSF/DOE/APC Future Modeling in Composites Molding Processes

Workshop

John P. CoulterProfessor and Associate Dean

P.C. Rossin College of Engineering and Applied ScienceLehigh University

Bethlehem, Pennsylvania 18015

Research Activities Related to Flow Processes During

Composite Manufacturing

Materials & Measurements Processing & Manufacturing

Sensors and ControlsProperties & 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

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

40 45 50 55 60

Ultimate Tensile Strength (MPa)

Pro

babi

lity

Den

sity

100% Virgin PS (IM)50% Virgin / 50% Recycled PS (VAIM)75% Virgin / 25% Recycled PS (VAIM)100% Virgin PS (VAIM)

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

CHIP

SUBSTRATE

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

t = 4 min

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

Neural Network Control of Autoclave Cure

Heated Air

tool plate

sensor composite

lay-up assembly

DEVELOPMENT

Product Function(cost)

Product Design:shape, material.

Process Selection:(casting, injection molding, autoclave cure, etc.)

ConditionsInitial ProcessingProcess

Cycle Database

COMPONENT FABRICATION

On-Line Product/ProcessQuality Monitoring

Real-Time Decision-Making Tool

Judicious ProcessingCondition Adjustment

Economically Manufactured Quality Product

Process ModelReliable

Input Layer

Hidden Layer

Output Layer

predicted quantities

fully connected

fully connected

i+1ln(i+1) i+1

bias Ti i ln(i) i

relevant parameters iterativelydetermined quantity

Tavg Tavg Ti+1

Input Layer

Hidden Layer

Output Layer

predicted quantity

fully connected

fully connected

i+1ln(i+1) i+1bias Ti i ln(i) i

relevant parameters desired values

Tavg Tavg

Ti+1

“Inverse” Neural Network Structure

“Forward” Neural Network Structure

-0.2

0.2

0.6

1.0

0 40 80 120

time (min)

degree of cure

degree of compaction

reducedviscosity

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

DEVELOPMENT

Product Function(cost)

Product Design:shape, material.

Process Selection:(casting, injection molding, autoclave cure, etc.)

ConditionsInitial ProcessingProcess

Cycle Database

COMPONENT FABRICATION

On-Line Product/ProcessQuality Monitoring

Real-Time Decision-Making Tool

Judicious ProcessingCondition Adjustment

Economically Manufactured Quality Product

Process ModelReliable

Process ModificationSubsystems

Product Quality Sensing

Subsystems

Intelligent Manufacturing Science Research:Perceived Gaps

Production on Target Machines

Optimal Control Subsystems

AppropriateIntegration 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