Fused deposition modelling (FDM) has become a prevalent technique to additively manufacture polymer that can provide design freedom and creativity. However, like any other AM technologies, FDM has its own challenges. The inherent void, surface roughness and dimensional accuracy during manufacturing can lead to tolerance rejection, cracking or failure during the product life. Also, the resultant anisotropic material properties are inherent in the layer-by-layer manufacturing features. Therefore, it is important to understand and simulate FDM process, in which complex thermo-mechanical interaction takes place due to rapid heating/cooling. The current work will first provide a brief overview of the current polymer AM simulation solutions, validation protocols, and standardization efforts. Secondly, a simulation framework using Abaqus will be presented to replicate the FDM process that can provide insight into how the process parameters affect product quality. Currently, simulation solutions has the capability to model the polymer extrusion process with that can capture the layer-by-layer element activation feature and varying free surface in heating and cooling. Finally, an example study will be presented to show how modifying process parameters such as raster angle, contour width and layer thickness affect the transient thermal, deformation and residual stress field. Accordingly, optimal processing parameters can be identified based on the criterion of minimizing distortion and residual stresses.

1. Simulation of Transient Temperature and
Stress Field in The Polymer Extrusion Additive
Manufacturing Processes
Ellie Ai Vineyard, PhD, Arindam Chakraborty, PhD, PE
Virtual Integrated Analytics Solutions (VIAS)
www.viascorp.com
Jan 18, 2018

2. © 2017 Virtual Integrated Analytics Solutions Inc.
Agenda
1 VIAS Overview
2 Polymer AM – Brief History and Current Status
3 Polymer AM Simulation - Standardization & Validation
4 Polymer AM Design and Process Simulation - Overview
5 Case Studies
6 Current Challenges and Future Direction
7 Q&A
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3. VIAS OVERVIEW

4. © 2017 Virtual Integrated Analytics Solutions Inc.
Company Overview
Engineering
Services
Training
Hardware
Software
• Multiple Industry Experience
• Presence in Houston (Main Office), Chicago, Cincinnati,
Detroit, San Francisco,
• Team consists of Ph.D. and Masters in Solid Mechanics,
Fluid Mechanics, Materials and Corrosion, Numerical
Analysis, Statistics; Optimization and Reliability
• Solution partner of Dassault Systèmes SIMULIA products
– Abaqus, Isight, fe-safe, Tosca
• Provide Virtual Design Experience through Collaboration
and Data Analytics – Provides Automation and
Customization
• Provide 3D Printing and AM Simulation Services
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5. © 2017 Virtual Integrated Analytics Solutions Inc.
VIAS Simulation Capabilities
FEA (Non-linear Material, Thermo-Mechanical, Cyclic Load, HPHT)
Fatigue-Fracture / Damage Mechanics
Polymer Modelling
Reliability and Optimization
CFD and Multi-physics Simulation
Root Cause Analysis
Data Analytics
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6. POLYMER AM – BRIEF HISTORY
AND CURRENT STATUS

7. © 2017 Virtual Integrated Analytics Solutions Inc.
Additive Manufacturing
A process of joining materials to make objects from 3D
model data, usually layer upon layer, as opposed to
subtractive manufacturing fabrication methodologies
[ISO 17296-1 and ASTM 2792-12]
Design Freedom Product Innovation
Bio-inspired Generative Design
• Early AM processes established in
the mid 1980’s as a solution for
faster product prototype
development
• Plastic processing techniques
were initially commercialized for
the development of “short life
products”
• Metal AM processes were
developed and introduced into the
market in the 1990’s
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8. AM vs Subtractive Manufacturing
8© 2017 Virtual Integrated Analytics Solutions Inc.
AM also gives us the power to manufacture more
customized and complex shapes in comparison to
subtractive manufacturing

9. AM vs Subtractive Manufacturing
9© 2017 Virtual Integrated Analytics Solutions Inc.
Additive Manufacturing
Adding material layer by
layer
Can create increasingly
complex / innovative shapes
Higher manufacturing times
Higher Capital Investment
Reduces raw material
wastage significantly
Lower range of materials
Subtractive Manufacturing
Removal of unwanted
material
Comparatively lower
complexity shapes
Lower manufacturing times
(simple shapes)
Lower Capital Investment in
comparison
Significant raw material
wastage
Larger range of raw materials

10. © 2017 Virtual Integrated Analytics Solutions Inc.
1986
Stereolithography
(SLA)
1989
Selective Laser
Sintering
( SLS)
1992
Fused
Deposition
Modeling
(FDM)
1993
Material Jetting
2014
Continuous Liquid
Interface Production
(CLIP)
2016
Multi Jet Fusion
(MJF)
Polymer AM History:
1
0

11. © 2017 Virtual Integrated Analytics Solutions Inc.
Categories of Polymer AM Processes
Vat
Photopolymerization
Stereolithography
Cured by Laser
Digital Light Processing
Cured by light/Projector
Continuous Digital
Light Processing
Cured by LED and Oxygen
1) The build platform is
lowered from the top of
the resin vat downwards
by the layer thickness
2) Light exposure cures the
resin layer by layer
3) After completion, the vat
is drained of resin and
the object removed
Material Extrusion
Fused Deposition
Modeling
Fused filament
Fabrication
1) The nozzles deposit
semi-molten material
onto the cross sectional
area
2) Filaments are fused
together upon
deposition
3) Materials are added
layer by layer
Vat Photopolymerization
Material Extrusion
1
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12. © 2017 Virtual Integrated Analytics Solutions Inc.
Categories of Polymer AM Processes
Material Jetting
Material Jetting
Drop on Demand
1) Droplets of material
are deposited from
the print head onto
surface.
2) Layers are allowed to
cool and harden or
are cured by UV light.
3) Post processing
includes removal of
support material.
Material Jetting
Powder Bed Fusion
Direct Metal Laser
Sintering
Selective Laser
Sintering
Electron Beam
Melting
1) Powder material is spread
over the build platform
layer by layer through
roller movement.
2) A laser fuses the powder
layer by layer.
3) The process repeats until
the entire model is
created.
Powder Bed Fusion
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13. POLYMER AM SIMULATION –
STANDARIZATION AND VALIDATION

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AM Standardization
Design
Design Rules
Material
Photopolymer Resins
Polymer Powders
Polymer Filaments
Process
Material Jetting
Material Extrusion
Power Bed Fusion
Performance
Mechanical Test
Methods
Post-Processing
Methods
Part
Quality
• NIST- Roadmap for AM: Metal and
Polymer (May 2013)
• ASTM International Committee F42 on
Additive Manufacturing Technologies was
organized by industry in 2009
• ISO/TC 261: Joint effort with ASTM since
2011
• America Makes & ANSI Additive
Manufacturing Standardization
Collaborative (AMSC), launched in March
2016
Purpose of Standardization and Validation:
• Efficiency - Reduces potential for redundancies and incompatibilities
• Consistency – Ensures the consistency and quality of AM parts
• Organization – Prioritization and planning of standards development is easier, and
relationships between standards are clear
Current Efforts
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Verification and Validation of AM Products
Medical
Devices
U.S. Food and Drug Administration (FDA) Guidance on Technical
Considerations for AM Devices
There is a recognized lack of guidelines for how to qualify & certify both AM processes
and finished AM parts.
Measurement Technology Simulation and Modeling
Defense and
Aerospace
o Nondestructive Testing (NDT)
o Computed Tomography (CT) Scanning
o Mechanical Testing
o Corrosion Testing
The Composite Materials Handbook-17 (CMH-17)
o Advanced Thermo-mechanical Model
o Accurate Constitutive Model
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16. POLYMER AM DESIGN
AND PROCESS
SIMULATION - OVERVIEW

17. © 2017 Virtual Integrated Analytics Solutions Inc.
Structural Design
Organic structures
optimized in print
direction
Optimize shape for
stress reduction
Non-parametric
lattice sizing for fixed
design spaces
Powerful
reconstruction for
final design
Member size
optimized for
postprocessing
Light weighted and
functional
From Concepts to Functional parts
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18. © 2017 Virtual Integrated Analytics Solutions Inc.
Unifying Modeling, Simulation and Optimization
in a single environment
• Efficient Product Engineering, removing bottlenecks that
usually make it cost-prohibitive to explore optimized parts.
• Intuitive workflow for designers , with non-expert solutions
• Automatic generation of function - driven conceptual shapes
and detailed organic shapes
• Seamless Collaboration with designers, simulation and
manufacturing engineers.
Functional Generative Design
Design for Additive Manufacturing (DFAM)
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Addressing Key AM Challenges through Simulation
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• Understand residual stress and
distortion
• Minimize the gap between the
designed and manufactured part
through process optimization
Optimize the Process
• Evaluate how the manufactured
part will perform under realistic
loading conditions in assembly
with other components
In-service Performance
• Generate topology that meets
functional requirements through
optimization
Generate a Functional Design
• Create and optimize a lattice
structure
Generate a Lattice Structure
• Develop confidence in raw and
processed properties
• Capture phase transformations
to understand actual
performance
Calibrate the Material

20. © 2017 Virtual Integrated Analytics Solutions Inc.
“As-Designed” Part
• Designed geometry without
stresses or distortions
• Standard material property
assumptions
Process Gap
• Materials
• Deposition Path
• Build Definition
• Heat Input
• Residual Stresses
• Distortions
• Altered Properties
“As-Manufactured” Part
• Residual stresses built up from
thermal process
• Deformations causing tolerance
issues
• Material properties are a function of
manufacturing process
Gap between “As – Designed “ and “As – Manufactured”
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Minimizing the Gap
• Designed geometry without
stresses or distortions
• Standard material property
assumptions
Process Gap
“As-Designed” Part “As-Manufactured” Part
• Materials
• Deposition Path
• Build Definition
• Heat Input
• Residual Stresses
• Distortions
• Altered Properties
Calibrated Process

22. © 2016 Virtual Integrated Analytics Solutions Inc.
Bridging the scales for Metal based Processes
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Length(m)
10-9
10-7
10--5
10-3
10-1
TTT
Phase Diagrams
Pure metal properties
Mechanical/Thermal
Alloy properties
Mechanical/Thermal
100
Calibrate continuum models
Homogenization
Coupon-level AM
simulationPhase Field
BIOVIA
𝒇 𝜶 𝒕 = 𝟏 − 𝒆𝒙𝒑 −𝒌 𝑻 𝒕 𝒏 𝑻 𝒇 𝜶
𝒆𝒒
𝑻
𝒇 𝜶′ 𝑻 = 𝟏 − 𝒆𝒙𝒑[−𝜸 𝑴 𝒔 − 𝑻 ] HAZ Prediction
AM part simulation
Heat treatment
Final properties
Micro-Scale
Polycrystal/Phase
transformation
Meso-Scale
Macro-Scale
10-9
10-8 10-7 10-6
10-5 10-310-4 10-2 10-1
Time(s)

23. © 2016 Virtual Integrated Analytics Solutions Inc.© 2016 Virtual Integrated Analytics Solutions Inc.
Optimization in AM
© 2017 Virtual Integrated Analytics Solutions Inc.
• Simulation software (TOSCA) can serve as a powerful topology optimization
tool for producing design concepts that often can’t be manufactured using
traditional techniques
• These designs are designed by the functional requirements of the part
• AM simulations make topology optimization results accessible for
production
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24. © 2017 Virtual Integrated Analytics Solutions Inc.
Optimize the
Process
• For Cost
• For Build Quality
Calibrate Material
and Build
• Improve material
response & design
Simulate Stress
and Distortion • Calculate Tolerance
Capture Physics
• Process Specifics
• Relevant phenomena
Physics Based Process Simulation Goals
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25. © 2017 Virtual Integrated Analytics Solutions Inc.
Multiscale, Multiphysics Simulation
Rapidly Changing Physics:
• Convection
• Radiation
• Evolving Free Surfaces
Energy source and application:
• Scanning lasers, semi-molten filaments, etc
• Localized heating: Over milli/micro seconds
• Full part print: Over hours
Material Evolution:
• Thin powder layers
• Metallurgical transformations
• Grain nucleation and growth
• Computationally efficient
Part-level simulation :
• Agnostic and open
• Highly customizable
• Use build data from machine
• Partial finite element integration
Polymer AM Simulation
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26. © 2017 Virtual Integrated Analytics Solutions Inc.
Goal: Apply predictive analytics to reduce part stress and distortion, minimize part time and
increase dimensional accuracy
❖ Build orientation optimization ❖ Support structure optimization
❖ Path Optimization ❖ Addressing print failure
Process Automation and Optimization
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27. CASE STUDIES

28. © 2017 Virtual Integrated Analytics Solutions Inc.
Multi-scale Infill Homogenization/ Polymer Extrusion
Infill optimization of a shoe sole using Representative Volume Elements (RVEs)
Use clustering technique to fix
design variable Pressure Loads from walking
Redistributed material using infill
optimization
Final Reconstructed
Infill
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Micromechanics Workflow
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Simulation for Evolving Technologies: Polymer AM
Part level simulation with support structures
Tool Path information from slicer (GrabCad)
Warping Effects – Mobius Arm
Material Orientations during layup
Abaqus Thermal Model vs
Thermal Imaging Data
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31. © 2017 Virtual Integrated Analytics Solutions Inc.
Big Area Additive Manufacturing/ Polymer Extrusion
Material: 13vol% / 20wt% Carbon Fiber reinforced ABS
Higher Conductivity, stiffness in bead direction
Lower CTE in bead direction
Abaqus/Standard Heat Transfer Thermal Imaging Data
Experimental Data Courtesy:
Oak Ridge National Laboratory
US. Dept. of Energy
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32. CURRENT CHALLENGES AND
FUTURE DIRECTION

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Uncertainties in Polymer AM Process Simulation
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• Constitutive model for different sets of
processing parameters
• Interfacial properties for layers, phases
and multi-materials
Most additive manufacturing processes
significantly impact the material properties
Changes in the material properties are the
result of microstructural revolution that need
to be captured
Performance of additively manufactured parts is
greatly determined by the material properties
• Failure / Fracture / Delamination • Durability / Creep under loading cycles
Challenges in Polymer AM Material Characterization
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35. © 2017 Virtual Integrated Analytics Solutions Inc.
Integrated Modelling Approach
Coupled Thermal-Mechanical
Modeling of the Process for
Thermal History and Stress State
Modeling of Microstructural
Evolution During Polymer
Extrusion/Material
Jetting/Power Bed Fusion
Effect of Resultant
Microstructure and Phases
on Properties and
Characteristics
• Thermal history induced delamination
• Progressive damage and fracture analysis
• Internal and surface defects
• Conductive, convective, and radiative heat transfer
• Energy deposition in porous and powder material
• Solid-Solid Phase Transformation under thermo -
mechanical cycling
• Non-equilibrium materials and processes
Challenges in Polymer AM Process Modelling
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Material development
•Broaden the selection of suitable
materials and establish a database
of mechanical properties of parts
fabricated by AM
Software tools specific to AM
•CAD design software tools
• FEM/CFD analysis software tools for AM process
•Post-processing analysis tools
Characterization and
certification:
•Ensure the repeatability and consistency
of the manufactured parts
•Ensure the part quality
AM process controlling
•in situ measurements of the temperature, cooling
rate, and residual stress
•in-process monitoring of geometric dimensions and
the surface quality of finished layers
Future of Polymer AM
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37. © 2017 Virtual Integrated Analytics Solutions Inc.
• Integrated computed material model to take the complex physic
phenomenon into consideration
• Benchmark additive manufacturing property characterization data
and eliminating variability in “as-built” material properties
• Post-processing guidelines and specifications
• Enable faster, more accurate, and higher detail resolution additive
manufacturing machines with larger build volumes and improved
“as-built” part quality
• Supercomputing capabilities
Future Path for Polymer AM Process Simulation
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38. Q&A
THANK YOU!