INTEGRATION OF MATERIALS MODELING IN
BUSINESS DECISION
APPLICATION TO COMPOSITE MATERIALS AND MANUFACTURING PROCESSES
EuroNanoForum 2017.
Valetta, Malta
H2020 COMPOSELECTOR PROJECT. Salim Belouettar
Decision(s) Making for a Tire
Image from MSC
Decision(s) Making for a Tire
3
Database connectivity
Manufacturing
Cost, access to the technology, Complexity
Flexibility, Time, Variability
Rubber composite
Material solution
Availability of material, cost
Customer
Volume, price, market segment, margins …
(Inter/multi)disciplinartiy
Multiscale and Multiphysics
Modelling and Simulation
Uncertainty
Quantification and
Management
Computational
Tools
Decision‐based
multi-objective
systems design
PM Cs Selection for
Aerospace and Transport
Markets
Multi disciplinary Design Optimisation, Uncertainty
Management and Model Selection
Databases
management
Verification and Validation ‐
Experiment/Model coupling
Data sciences
and material
informatics
Business Decision
System
Digital Data
Data Integration
Experimental
Inputs
Interoperability and Metadata
Material & Process
Modelling Farmework
Models for
manufacturing
Green Design and
Life Cycle
Engineering
complex INTERACTIONS
INTERACTIONS BETWEEN MATERIALS MODELLING
STAKEHOLDERS IS OFTEN THWARTED BY
COMPLEXITY.
SOFTWARE
PROVIDERS
EXPERIMENTALISTS
MATHEMATI
CIANS
ACADEMIA
INDUSTRIAL
USERS
MODELLING
Need of Common
Language …
ACTIVITY
PHYSICAL MODEL
MODELLING
SOFTWARE
DEVELOPPERS
EXPERIMENTAL EVIDENCE
(USER CASE)
e.g. material, process,
phenomenon
Equations describing the
behaviour of chosen entities
(equations for physical
quantities)
MODA
POST-PROCESSING
Translation of the results in
a more convenient humanunderstandable format.
NUMERICAL SOLVER
Methods and software for
the solution of
the PE+MR.


Need of a Paradigm Shift: Decision should be
objective as possible and less/not subjective
 increase the fraction of critical decisions
informed by modeling and simulation
MODELLING as a node of the glabal BUSINESS
WORKFLOW Chain
decision
making
human
interaction
databases
Simulation
What is needed
Decision require workflows involving different types of models and their coupling
and linking. MODA- EMMC (www.emmc.info)
Decision is typically undertaken in the context of product
development/enhancement – coupling with manufacturing is natural.
Need to optimize concurrently materials and process contextually with the
effective use of materials models at different scales in the selection procedure.
Need for managing uncertainty and its propagation through model/experiment
and business chains.
Need for interoperability standards
Workflows involving different types of models and
their coupling and linking. MODA- EMMC
Characterisation, design and elaboration
Characterization
and design
Molecular scales
Characterisation, design and
elaboration
Interface/interphase
1m – 100µm
100µm – 10µm
10µm – 0.1µm
0.1µm – 10nm
Representative service
conditions
Microstructure
complexity
Phase morphologies
Interface behaviour and
nano-reinforcement
Data acquisition
Matrix
Reinforcement
particles, filers,
fibers
Modeling Data (MODA)
CONTINUUM
MODELS
COARSE
GRAINED
MOLECULAR
DYNAMICS
AB INITIO
Data Compression
Meso-Micro Scale
Macroscale
Coupling with manufacturing Process
Process
Quality
Control
Control
Optimisation
Manufacturing Decision making
Quality
Quality
Process
Control
Quality
Creteria
Control
Robustness
Productivity
Complexity
Optimisation
Variables «Process»
Variables « Machine »
Analogy with the Maslow pyramid
Decision making
Need to optimize concurrently materials,
process and Business (MDO)
Tes ng
Valida on
Manufacturing
Computed
prototype
Material
Proper es
Valida on
Tes ng
DATA DRIVEN SIMULATION
COMPUTATION
Material
Proper es
SENSITIVITY
Experimental
prototype
ERROR ESTIMATION
Manufacturing
OPTIMISATION
MULTISCAL & MULTIPHISCAL
COMPOSITE MATERAIL SYSTEM
GREEN DESIGN AND LCE
Managing uncertainty and its propagation through
model/experiment and business chains.
Di
gi
M
m
at
M
F,
M
m
at
M
F,
M
X
X
3
Incertainties are the key drivers of model
adaptivity
Uncertainty
M
3
Di
gi
P
P A AMF
D
C
IST M-R OR
CO
DI O M
TM M ,
O
P
G
RT ,
IM OL
D I M PO
IO PA
GI
AT AB
N M
M L AB
&
C
AT
AE
FE
Minimum
Uncertainty
Model
Uncertainty
Parameter
Uncertainty
Complexity
M
UL
2
Need for interoperability standards
PM Cs Selection for
Aerospace and Transport
Markets
Multi disciplinary Design Optimisation, Uncertainty
Management and Model Selection
Business Decision
System
Data Integration
Interoperability and Metadata
Material & Process
Modelling Farmework
The success of such integration
heavily relies on the openness of
application interfaces, the
maturity of various software tools.
Presentation of my colleague
Adham Hashibon
DECISION SUPPORT APPS
LCE, Light Weighting, Visualization Costing,
Res Subs Assessment. etc…
H020 Composelector Concept
MATERIAL WORKFLOW FRAMEWORK
Di
gi
M
M
Di
gi
m
3
at
M
F,
M
m
at
M
F,
M
P
P A AMF
D
C
IST M-R OR
CO
DI O M
TM M ,
O
P
G
RT ,
IM OL
D I M PO
IO PA
GI
AT AB
N M
M L AB
&
C
AT
AE
FE
X
X
3
K ij
Reconstruction
Shape,
Orientation,
Distribution
Analysis and
Design
M
UL
2
EXAMPLES … USERS CASES
DeMEASS VIII
H2020 BNMP.
www.composelector.net
AIRBUS: Airplane frame
Application of the BDSS for Material and Process
Selection of fuselage thermoplastic frame
Composite materials complexity
19
Detailed design: non linear FEA and op misa on
micro-meso levels
Valida on
Macro level
Preliminary design
Conceptual design- rapid sizing
Coupon level-ply
proper es
characteriza on
Meso-macro
Structure centric
(sizing)
Material centric
(sizing)
Manufacturing
Process centric
(CAM, CAE, CFD)
Design centric
(CAD)
Material selec on
micro-meso
Fibre/resine/
inteface
Manufacturing process
tes ng
(physical and numerical)
Permeability
Flow, defects
Element level
Characterisa on
Component
Level
Characteriza on
Numerical/Exp.
Numerical/Exp
Assemblies
tes ng
Numerical/Exp
Full part level
Physical and
numerical
tes ngs
Op misa on of fib r
placement
(e.g. stacking
sequence) or design
(shape, topology)
micro-macro
approach
•
•
•
•
•
Manufacturing Process
Resin flow (CFD)
Curing (CAE)
Thermoforming (CAE)
Fiber placement (CAD/
CAE)
Op misa on of process.
Component Level
Draping
Assemblies
Full part level
Composite materials complexity
20
Detailed design: non linear FEA and op misa on
micro-meso levels
Valida on
Macro level
Preliminary design
Conceptual design- rapid sizing
Coupon level-ply
proper es
characteriza on
Meso-macro
Structure centric
(sizing)
Material centric
(sizing)
Manufacturing
Process centric
(CAM, CAE, CFD)
Design centric
(CAD)
Material selec on
micro-meso
Fibre/resine/
inteface
Manufacturing process
tes ng
(physical and numerical)
Permeability
Flow, defects
Element level
Characterisa on
Component
Level
Characteriza on
Numerical/Exp.
Numerical/Exp
Assemblies
tes ng
Numerical/Exp
Full part level
Physical and
numerical
tes ngs
Op misa on of fib r
placement
(e.g. stacking
sequence) or design
(shape, topology)
micro-macro
approach
•
•
•
•
•
Manufacturing Process
Resin flow (CFD)
Curing (CAE)
Thermoforming (CAE)
Fiber placement (CAD/
CAE)
Op misa on of process.
Component Level
Draping
Assemblies
Full part level
KPIs Definition for Material Selection
Composite Leaf Spring
Simulation Workflow
•
Binder
Rheology
Model
(Modeling) TP
Binder
Characteristics
Loads, Boundary Conditions
Binder
placement
Modeling UD
Dry Fibre
Preforming
Characteristics
Micromechanics RVE
(Leuven Software?/Digimat)
•
•
UD Fibre
Bundel
Mechanics
Permeability
Model
•
•
Initial Preforming
Tool Design,
Molding Conditions
Initial HP-RTM
Tool Design,
Molding Conditions,
Resin Rheo-Kinetics*
Molding Conditions,
Resin Kinetics*
Leaf Spring
Stiffness/
Strength
Modeling
•
•
•
Stiffness Response vs
design target
Strength safety margin
Temperature Dependency
ESI / MSC
Modeling
Preforming
Modeling HPRTM
Infusion**
Modeling HPRTM Curing /
Demoulding
Mechanical
Modeling
Local Part
PAM-FORM
PAM-RTM
PAM-RTM
DIGIMAT
Pre-form UD-Fibre
distribution
Pre-form Permeability
* RheoKinetic Modeling? or from tests?
** Incl cure development during infusion
*** PAM-RTM?
•
•
•
•
Infusion pattern
Pressures
Infusion Time
Fibre movement***
•
•
•
•
Cure Time
Cure Development
Residual Stresses
Part geometry after
demoulding/
Shrinkage
•
•
•
•
•
•
•
Material Usage
Weight
Cycle Time
Tooling Cost
Capital
Energy Cost
# of operators
•
•
•
Local non-linear
Stiffness
Local Strength
Temperature
Dependency
Cost, Value,
Risk Modeling
Cost &NPV Models
KPIs
-
-
-
-
Weight requirements & weight reduction value
(assume 5 Euro/kg)
Production Scenarios (target, low, high volume
scenario), lifetime
Production cost target (Capital, Running Costs).
Manufacturing targets: cycle time options (tooling)
Material cost scenarios
LINKS ..

Important links:
EMMC: The European Materials Modelling Council
(http://emmc.info)
 MODA: Modeling Data:


Short version of RoMM VI. https://bookshop.europa.eu/en/whatmakes-a- material-function--pbKI0417104/. Edited by Anne F de
Baas

Vocabulary, classification and metadata for materials modelling (130 FP7
and H2020 projects)
RoMM
VI
Review of Materials Modelling
(RoMM version V and VI)
http://ec.europa.eu/research/industrial_technologies/modelling-materials_en.html
Acknowledgements ..
The funding from the European Union’s
Horizon 2020 research and innovation
program under grant agreement No
721105 is acknowledged.
The author acknowledge the
contribution of all the
COMPOSELECTOR project partners.

Thank you !