Simulation of Draping, Infiltration and Curing of Composites

Simulation of Draping, Infiltration and Curing of Composites

Dr. R. M. Hinterhölzl, FACC Technical Colloquium “Advances in Composites”

5./6.7.2012, Salzburg / Austria

• Introduction to the LCC –Institute of Carbon Composite

• Process Simulation –Why?

• Simulation of the

• Draping Process

• Braiding Process

• Automated Fiber Placement (AFP) Process

• Simulation of the Liquid Composite Moulding(LCM) Process

• Curing Simulation – Process Induced Deformation (PID)

• Simulation Platform

• Conclusions and Outlook

5./6.7.2012 2

Overview


Institute for Carbon Composites

5./6.7.2012 3

• Founded in Mai 2009 financially supported by the SGL Carbon Group • Professor Dr.-Ing. Klaus Drechsler • 52 researchers • 2 team assistants • 5 Technical Employees

Main Research Topics:


Industrial Partners

5./6.7.2012 4

Lehrstuhl für Carbon Composites


5./6.7.2012 5

• Aviation industry

approx. 50% of the structure are made out of Composite: fuselage, frames, stringers, skins,…

High volume

Bigger parts

Need for process optimization

Need for automation

• Automobile industry

Serial production with very high volume, short cycle time, robust processes

further and new developments and optimization of manufacturing processes

From hand lay up to (partly) automated serial production

Automation Shorter cycle times Process reliability Short design and development time


Manufacturing Process Simulation – Why?

5./6.7.2012 6

semi-finished part

preforming (here: draping)

curing

matrix-injection

component

Simulation of composites

Semi-finished part structure: micro-/mesomechanics

FE draping simulation

LCM simulation: flow front

Structural component simulation

Curing- and distortion simulation

[FACC AG]


Draping Simulation

5./6.7.2012 7 Simulation of Draping, Infiltration and Curing of Composites

• Part design (design to process) – Prediction of fiber alignment – Prediction of fiber volume fraction – …

• Process development / optimization – Drapeability inspection (feasibility study) – Prediction of draping defects – Geometry and alignment of the flat preform – …

• Embedding in process simulation platform – Basis for: – LCM simulation – Curing and PID simulation – Structural simulation

5./6.7.2012

Goals of Draping Simulation

lay-up of the preform

tool preform

Forming (draping) in required shape

Airbus A380 – Pressure Bulkhead

8 Simulation of Draping, Infiltration and Curing of Composites

• Textiles – woven, Non-Crimp Fabrics (NCF), UD, etc. – dry or pre-impregnated

• Deformation modes

• Defects

5./6.7.2012

Requirements for Draping Simulation

plain weave twill weave NCF

Shear Intra-ply Motion

Wrinkling / Folds

Inter-ply Motion Elongation Bending

Loops Gaps / Voids Fiber Pull-Out Damage of Stitching


Finite Element Approach

• modeling of the preform on ply-level with shell elements

• simulation of the draping process and the tools

• consideration of forces, friction, velocities, etc.

• prediction of deformation modes and defects in the preforms

Kinematic Approach

• pin-joint method [Mack 1950]

• purely geometrical approach

• computation of the fiber direction

• direct cut geometry generation

Numerical Approaches for Draping Simulation

5./6.7.2012

level o

f detail

L1 L2

P

shear

bending elongation


Kinematic Approach

+ fast & easy to use + many software available + good interface to structural analysis - no material behavior - no process influence - poor results for complex shapes

Comparison of the Different Approaches

Finite Element Approach + accurate results even for complex shapes + accounts for material and process influence + prediction of draping defects possible (wrinkles, gaps, etc.) - higher computational effort - material testing required

Catia CPD (Kinematic Simulation)

Experiment PAM-FORM (FE Simulation)


Simulation Procedure

Validation

Process Simulation

Parameter Determination

Material characterization (simulation of the material behavior)

• Picture Frame Test

• …

Validation on test geometry

• Hemisphere

• Double Dome

• …

Process simulation (design and optimization)

• Diaphragm draping

• Drape forming

• Hand lay-up

• …

Saertex

ECD

5./6.7.2012 Kap.-12 Simulation of Draping, Infiltration and Curing of Composites

Prediction of • Draping defects

• Positioning of darts and stitching

• Flat pattern

• Material and process characteristics

• …

To be used in • Part design / development

• Process development or optimization

• Composite process simulation platform

• …

5./6.7.2012

Profits of Draping Simulation


Drapingsimulation.avi

Simulation of the Automated Fiber Placement Process (AFP)


5./6.7.2012 15

Overview – Simulation of the AFP – Process

Placement – scenario

Therm. and mech. material models Simulation Model Validation

[LCC]


Research Activities at the LCC

5./6.7.2012 16

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

ε[Δ

l / l]

Zeit [s]

1D – Simulation: • Calculation of the time-dependent

mech. compaction behaviour • Simulation of the transient heat

transfer

2D – Simulation: • Prediction of the mech. and therm. behaviour

of the process • Simulation of bridging of the tape • Modeling of tack

3D – Simulation: • Mech. placement on complex geometry

possible • Prediction of the complete thermal history

of the placement process


• Gap- / Overlap detection

• Compaction / Debulking

• Bridging

• Complete temperature distribution

AFP – Process Simulation: Examples

5./6.7.2012 17

Fig. 1: Gap in Simulation

Fig. 3: Surface temperature distribution

Fig. 2: Compaction behaviour of a Slit-Tape


• Prediction of Bridging and Sliding

• Prediction of Gaps and Overlaps

• Simulation of the complete heat transfer

• Simulation of the Compaction behaviour results in a debulking prediction

• Optimization of all Process parameters, e.g. Compaction Force, Heat Output, Process Speed

• Linkage between all problems to find a reliable process window

Benefits of the AFP Process Simulation

5./6.7.2012 18

Fig. 1: Sim. Bridging

Fig. 2: Contact pressure and peel-force

Fig. 3: Thermodynamic simulation of a generic AFP – Process


5./6.7.2012 19

Braiding Process Simulation


• High volume production of composites – short cycle time, automated and robust processes

• Braiding technology – Preforming of fibers and subsequent resin infusion

– High degree of automation possible

• Overbraiding process – Bobbin carrier -> interlacing yarn pattern

– Braid deposits on mandrel

• Process offers high flexibility – Mandrel shape defines contour of stucture

– Braiding angle, areal weight, etc.

Braiding process – Introduction

Braiding machine and principle of braiding


Braided composites:

• Input: – production parameters and mandrel geometry

• Output: – Undulation, intersection of yarns, variable braiding angle

variable material properties (stiffness and strength)

Analysis requirments:

• Process influence on yarn architecture

• Yarn architecture influence on material properties: – Elastic behavior

– Failure behavior

Braiding process – Analysis of braided Composites

Variation of Braiding Angle on Component [FACC AG]


Estimation of the braid architecture for complex parts

• Polished specimen / image analysis

• Kinematic methods

• FE-braiding process simulation

Suitable meso-scale models

• Analytical methods

• Finite element unit cells

From meso- to macro-scale

• Transfer from unit cell to component

• Failure surface vs. meso modeling

• Data flow for braid architecture

Braiding Simulation – Perspective

σ2

σ1


FE Braiding Process Simulation

Yarns

• Explicit FE Model using Abaqus/Explicit

• Yarns:

– Truss elements

– Pretension

– BCs according to braiding configuration

• Yarn-yarn interaction

– Contact with friction

• Mandrel

– BC for motion calculated automatically

– No limitations in shape

• Model generation

– Python scripting inside Abaqus

– Mandrel movement calculated using MATLAB script

Springs: pretension

Braiding ring

Mandrel


Biaxial full braiding configuration:

FE Braiding Process Simulation

Yarn architecture: inner bend

Yarn architecture: outer bend

Yarn architecture: straight cylinder


5./6.7.2012 25

Simulation of Liquid Composite Moulding (LCM)

processes


Goals

• Prediction of process parameters

– Tool pressure

– Flow front velocity

– Fill time

– Prediction of defects (dry spots, pores)

– Cure kinetics / Heat transport

• Optimization of injection schemes

• Tool design

• Design of robust processes

Challenges

• Material characterization

– Preform

– Resin

• Capturing influences of preceding process steps (such as textile handling, preforming)

Motivation

5./6.7.2012 26

Fig.1: Sportscar Monocoque

Fig.2: Filling simulation (flow front plot) of a Sportscar Monocoque


5./6.7.2012 27

• Tool design – Securing expensive investments for RTM tools

e.g. reducing the risk of dry spots

• Estimation of process parameters Various geometries and fiber architectures

Prediction of

• process time

• flow front propagation

• imperfections

• Design of robust processes Simulating variations within the process chain

• Semi-finished product

• Preforming and Handling

• LCM process

Definition of Quality Gates for each step

Less scrap parts and more reproducible output

Profits of LCM Simulation

Fig.: RTM tool (source: Eurocopter)


Physics of LCM Processes

• Flow processes in porous media can be described by the Navier-Stokes equation

𝜌𝜕𝑣

𝜕𝑡= −𝛻𝑃 + 𝛻 ∙ 𝑇 + 𝑓

• By homegenization of the media (e.g. by volume averaging) and introducing some assumptions the flow can be described by Darcy‘s law

𝑣 =𝐾

𝜇𝛻𝑃 − 𝜌𝑔 ≈

𝐾

𝜇𝛻𝑃

• Darcy‘s law relates the flow front velocity 𝑣

to the applied pressure gradient 𝛻𝑃 with the proportionality constants permeability 𝐾

and viscosity 𝜇.

• Permeabiliy is a material parameter of the fiber preform

• Material

• Layup

• Fiber orientation

• Compaction state

5./6.7.2012 28

Fig.1: Illustration of flow fronts (Real vs. Darcy) within an textile preform

Tab.1: Summary of assumptions for validity of Darcy’s law

• Newtonian Fluid • Constant Viscosity • Homogeneous, Inelastic Porous Medium • Neglecting of Gravity and Capillary Forces • Low Flow Velocity (𝑅𝑒 < 1) • Saturated Flow • Neglecting Intra Yarn Flow


𝐾∗ =

𝐾11 0 00 𝐾22 00 0 𝐾33

(lat.: permeare = ‚to pass‘)

Description

In the 3D case the permeability can be noted as a 2nd order tensor.

The tensor can be diagonalized with a rotation of the global coordinate system to a coordinate system spanned by the major permeabilities (cf. Eq.1).

Determination

• Experimental

• 1D-flow method

• Radial-flow method (2D)

• 3D-flow method

• Numerical

5./6.7.2012 29

Permeability Testing for LCM Processes

Fig.3: Off-plane test setup Fig.2: In-plane test setup

Fig.1: 3D permeability tensor

ln-Plane components / 2D tensor

Off-Plane component/ through thickness perm.


Numerical Method - Overview

5./6.7.2012 30

„Grey Box“ Software Tool

(Matlab)

RTM Process Simulation (PAM-RTM)

Fabric Scan

Info

Material Card

Draping Simulation

Process Parameter

CAD

• Image Analysis • Textile Modeling • Permeability Calculation • Material Data

Fig.1: Fabric Scan Fig.2: Image Analysis Fig.3: Textile Model [WiseTex, KU Leuven]


Current fields of research are

1. Permeability Prediction

• For Various Textile Types

• With Respect to Shear State and Fiber Volume Content

2. Numerical Permeability Prediction

• Efficient Determination of Parameters

• Modeling of a Representative Unit Cell

3. Characterization of Variability of Textile Preforms

• Development Testing Methods

• Process Simulation to assess impact on LCM process

4. Characterization of Complex Preforms

• Thick laminates

• Radius

• Butt-splice, Overlaps

5. LCM Simulation of parts

5./6.7.2012 31

Outlook

Fig.1: Complex RTM component [FACC AG]


32 5./6.7.2012

Determination of Process Induced Deformations (PID) & Curing Simulation


• Geometrical deviations of stiff structures

Additional rework effort and / or high mounting forces required

Expensive iterative tooling adaption

5./6.7.2012 33

Potential on Dimensional Control of Stiff Structures

Picture: DLR

Deformed frame after curing Magnification factor: 30

Deformed frame section Magnification factor: 30


• primary:

• Determination of process induced deformations (dimensional accuracy)

• Evaluation of the effects of residual stress (structural analysis)

• secondary:

• process optimization (cycle time)

• process control (interaction between parallel simulation of physical curing process)

5./6.7.2012 34

Curing simulation - goals

Shrinkage Spring-In / Spring -

Back

Warpage

PID Fernlund et al, 2003

Svanberg, 2002 Twigg, 2001


Methodologies for determination of process induced deformations (PID)

5./6.7.2012 35

Analytical: Estimation of deformation for simple geometries

Experimental: Iterative optimization of final part shape

Phenomenological: Summarization of different mechanisms with one actuating variable (enhanced CTE)

Curing simulation: Simulation of all relevant mechanisms (e.g. reaction kinetics, modulus development,…)

Increasing level of detail and modeling effort


Process simulation including curing: application flow on the example COMPRO

5./6.7.2012 36

Thermo-chemical module • reaction kinetics • heat conduction

Flow Module • resin flow due to laminate compaction (viscosity, permeability) → variations in wall thickness and fiber volume content

Stress module • modulus development related to resin degree of cure, temperature • resin cure shrinkage and thermal expansion coefficients

Figure: Time-Temperature-Transition-(TTT) Diagram

Figure: modulus development

Figure: Experimental determination of effective HTC

Virtual autoclave • heat transfer autoclave gas → part-tooling-assembly • feedback control autoclave

Figure: Force-Displacement-Diagram Fiber Bed Compaction

control routine – status variables ( ,T)

COMPRO is a commercial composite processing software: Convergent Manufacturing Technologies, Vancouver, Canada

Johnston A., Hubert P., 1996


• Quantities:

• Deformations

• Residual stress

• Spatial distribution of fiber volume fraction

• Time history of temperature evolution on the part

• Final degree of cure distribution

• Glass transition temperatures

• Assessment a priori concerning:

• Mounting tolerances, dimensions of tooling

• Load bearing capacity

• Part’s quality (sufficient degree of cure, glass transition temperatures, hot spots (resin degradation))

• Optimization via variation of

• Geometry

• Material selection

• Lay-up

• Process cycle

5./6.7.2012 37

Curing simulation - results

Spring-In of a generic C-frame after curing

Deformed frame section Magnification factor: 30

Temperature Distribution on a Frame Section at the End of 2nd Ramp [simulated utilizing Compro2D and Raven, Convergent Manufacturing Technologies, Vancouver, Canada]


Profits of Curing and PID Simulation

5./6.7.2012 38

• Production support - defining process boundary conditions for a robust design

– curing of thick walled components → identification and elimination of hot spots

– high cost savings on reduction of tool adoption

– reduction of rework due to geometrical misfit (especially stiff structures)

– reduced risk for the manufacturing of big components und small highly integral RTM parts

• Effective PID determination by tailoring to particular assembly needs

– appropriate model detail

– relevant material characterization

– adapted analysis approach

• Cure cycle time optimization (where no stringent cycle predetermined)


5./6.7.2012 39

Determination of PID – State of the Art on A Simple Example

Analytical Spring-In Determination Utilizing Lamina (1) and Laminate (2) Homogenization (e.g. [Radford, 1988])

Numerical Distortion Analysis Utilizing Sequential Thermo-Chemical (1) and Mechanical Analysis (2) [simulated utilizing Compro CCA, Convergent Manufacturing Technologies, Vancouver, Canada]

+ =

T

I αI, ϕI

αT, ϕT (1) (2)

Nodal temperatures, (1) Displacements, (2)

Temperature and Degree of cure history


5./6.7.2012 40

Activities on PID Determination

Calibration Application Characterization

Overall goal: effective application of process simulation for PID determination

• Resin cure kinetics • Glass transition • Modulus development • CTE • …

• Interaction calibration • Shear layer modulus • Cohesive modeling

• Determination of enhanced CTEs for different setups (e.g. sandwich)

• Applicability of enhanced CTE

• Boundary condition characterization

• Sensitivity analyses


41 5./6.7.2012

Simulation Platform


Simulation Platform

5./6.7.2012 Simulation of Draping, Infiltration and Curing of Composites 42

Material Modelling

Draping simulation

LCM simulation


structural component simulation

[FACC AG]


Material Modelling LCM simulation



CATIA CPD/AFM PAM-FORM PAM-Quickform ABAQUS

Draping simulation

ABAQUS NASTRAN

ABAQUS NASTRAN COMPRO (CMT)

PAM-RTM RTM Worx

Common Data XML Format

Mapping Tool

Material Database

Wrapping Tool

WiseTex

Simulation Platform

Draping Simulation for Process Simulation Platform


• Draping simulation as a part of the process simulation platform for composites

– Provide data for subsequent simulations

– Supply of: fiber alignment, shear angles, thickness, etc.

• Draping results are combined to a continuous simulation tool chain and connected to a structure simulation

Draping

Infiltration

Curing

Structure

5./6.7.2012 45

Process Simulation Platform - Example

Preforming

• Draping, Braiding, Filament winding, …

• Simulation of the preform process

Preform Characterization

• Experimental

• Numerical

LCM-Simulation

• Tool design, Process time prediction (Flow front velocity, pressure distribution)

• Process optimization

• Curing simulation (for PID investigations)


• Close interaction in-between production and simulation (process simulation and structural simulation) is strongly required

• Process simulation supports the development and optimization of the manufacturing process (cycle time, process robustness, etc.)

• Simulation of individual process steps (draping, LCM, curing,…) and their dependency on each other is possible

• Process simulation supports the part design

fibre orientation defines stiffness and strength of the part

• Goal is the simulation of the whole composite manufacturing process

5./6.7.2012 46

Conclusion


Outlook

5./6.7.2012 Kap.-47

Composite Process-Simulation

OEM

Engineering Office

Test Labaratory

LCC

Software

Software Houses

Customers – Application Centers

Production Engineering Supplier

Scientific Backbone

LCC


• FACC AG / PCCL - COMET-K1 projects IV-3.04 and 3.S2

• Eurocopter Deutschland GmbH – Funded research project HELIPLACE

• Eurocopter Deutschland GmbH - LUFO IV: CompTAB - Composite Tool Chain and Enhancement to As Built

• Eurocopter Deutschland GmbH - Funded research project Vi-TECH

• EADS-IW - LUFO IV: CompTAB/ ITComp: Integrated Toolchain for Simulation of Composites

• Audi AG

• General Electric (GE)

• BMBF Project MAIdesign within cluster of excellence MAICarbon

5./6.7.2012 48

Acknowledgements


5./6.7.2012 49

Thank you for your attention!

semi-finished part

preforming (here: draping)

curing

matrix-injection

component

Semi-finished part structure: micro-/mesomechanics

FE draping simulation

LCM simulation: flow front



[FACC AG]


Documents

Simulation of Draping, Infiltration and Curing of Composites