3dprintconfny14

1. Simulation of Additive
Manufacturing Technologies:
Enabling a 3D “Print Preview”
Brent Stucker, PhD
Founder & CEO
3DSIM, LLC

2. AM can now enable
…control of the overall geometry of a part, which could
be made up of a truss network, where each truss has an
optimized thickness and could have an individually
controllable microstructure or material.
• But we can’t efficiently:
• Design structures this complex in CAD
• Predict what our machines will do when we print
a new geometry we haven’t printed before
• Predict the differences between printing the
same part in two different locations/orientations
• Predict how different process parameters affect
accuracy, microstructure and part performance
Courtesy David Rosen, Georgia Tech

3. Typical Process Variation
Effects
• 2 mm wall made from
Inconel 625
– XZ section showing
effects of scan pattern
variation on
microstructure
• Identical geometries in
the same build give
different distortions

4. (left) Prior beta interfaces ~100 μm
wide show the hatch spacing
(right) Prior beta interfaces not visible
in the bottom layers: microstructure
changes orientation each layer.
(3DSIM predicted values for angular
distortion is ~12-19º, which are in the
observed range.)
• Horizontal Tensile Specimens in
the top (last to be processed) layers
• Horizontal Tensile Specimens
in the bottom (lowest) layers
4
200 X
Microstructural Variations
due to Orientation in Ti6/4
1000 X
200X Bottom, θleast =12°
200X Intermediate layers
θmax =19°

5. 5
200X Bottom
Horizontal samples
200X Bottom
Vertical samples
• Identical process parameters
for identical parts
in an identical layer,
in the same build,
for the same material, but
in different orientations and locations,
result in
different microstructures and properties
• Less residual stress in Vertical samples 
columnar grains
• High residual stress in Horizontal samples 
martensitic streaks
Microstructural Variations
due to Orientation in Ti6/4

6. The “Support” Problem in
Metal Laser Sintering
• Supports today are
placed based upon
geometric relationships
and user experience
– Extra supports increases
post-processing costs
– Supports can ruin key
features
– Under-supporting regions
causes blade crashes

7. The Current Situation
• We Need An Accurate 3D “Print Preview”
– Based upon Real Process Parameters & Scan Vectors
– To Give us Accurate Geometry Prediction
• Including Distortion and Where we Need Supports
– Internal Microstructure Predictions
– Properties & Performance Predictions
• But what we have today is…
– A CAD file and a “Preview” of 2D slices of a build
– A lot of experimental data to tell us what “might” or
“probably” will happen under different situations

8. What’s Wrong with
Existing Simulation Tools?
• Manufacturing simulations of the past were
developed with the idea that we can take a long
time to get the right answer because we’ll make a
lot of the same thing over and over…
– Most are based upon 20-30 year-old formulations
• They are not optimized for multi-physics, multi-
scale modeling or compatible with GPUs.
• They don’t have a unified computational
infrastructure that enables you to solve all parts of
the problem in one package.

9. • Process simulations that are faster than an AM machine
builds a part
– Predict residual stress and distortion so we know how to place
supports and how to pre-distort our CAD model
• Material simulations which can predict crystal level
details and the resulting mechanical properties
• Lightning fast solutions on GPU-based platforms
• We simulate only what we need to get a practical
answer as FAST as possible
Our Modeling Vision

10. Our Overall Approach
• Most Modeling Tools Link
Process Structure Properties
• We’ve developed two Separate Solvers:
– Process Solver gives – Process Structure
• Thermal history, distortion, residual stress, crystal structure…
– Material Solver gives – Structure Properties
• Based upon the crystal structure, what are the properties

11. Inputs
Material & Process
Information
Process Structure Solver
3DSIM
Process Solver
(a.k.a FFDAMRD)
Multi‐scale Finite Element Analysis
using Novel Meshing and New
Computational and Numerical
Techniques)
Outputs
Thermal History, Cooling Rates, Phase
Information, Residual Stress/Strain

12. Benefits of our Dynamic
Meshing Strategy
• Demonstrated to be 66x faster than
ANSYS for solving AM problems
• ANSYS assembles matrices and calculates
nodal connectivity (stiffness matrix) every
time-step
• Our “intelligent assembly” of matrices
solves an identical problem with no
recalculation of nodal connectivity
• Fine-scale mesh developed for a
particular energy source and/or machine
with no hanging nodes or improperly
skewed meshes

13. Our Core 3DSIM Code
• Formulated for moving
energy source problems
• Multi-scale mesh fits
any size geometry
– Nano-manufacturing to
meters of manufacturing
• Fits whatever energy
source size you choose
– Applicable to ‘n’ scales
of refinement

14. Top Surface Domain in the x direction
TopSurfaceDomainintheydirection
Thermal contours at arbitary time steps during 1st layer of Laser scanning
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 10
-3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
x 10
-3
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Unstable thermal contours at turns
Stable thermal
contours
Scan
Strategy
Simulation Results: Example
Thermal History

15. Effect of Powder Packing
Density on Melt Pool Geometry
(10%, 20%, 30%, 40%, 50%, 60%)

16. Material Solver
Multi‐scale Dislocation Density based
Crystal Plasticity Finite Element Solver
Inputs
Crystal structure (Euler angles &
dislocation density), thermal history
and mechanical loading information
(e.g. tensile/fatigue test).
Outputs
Dislocation Density history, stress/strain
curves, slip details, Modified Microstructure
(grain size, orientation, etc)
Structure Properties
Solver

17. Validation of Mechanical
Property Predictions
Ti64 Part Behavior-Tensile (EBM processing)

18. How difficult is the
Problem We Want to Model?
• Finite Element Modeling of a commercial full-scale build:
– 200mmx200mmx200mm powder bed size
– 10 microseconds time steps to capture melting
– 20 micron layer thickness
– 10 micron resolution small-scale mesh (2 elements/layer)
• 108 elements per layer, 1012 elements per build if fine meshed everywhere
– 50 hours of actual laser scan time
• 1010 total time steps

19. Time and Efficiency Comparisons
(assuming a 16 teraflops machine)
• BASED UPON OUR CALCULATIONS, WE PROJECT:
• Fine Gridding (using ANSYS or similar method) = 5.7 10 years
• ANSYS (with multi-scale) = 8.9 10 years (89 billion years)
• 3DSIM (with multi-scale) = 1.3 10 years (1.3 billion years)
– It will be much faster in C++, but not fast enough..
• This is why modeling experts only simulate simplified versions of the
problem
• We decided to keep trying to find faster ways to do the entire
problem…

20. Computational Efficiency Modules
Process
Solver
Material
Solver
Eigensolvers
Used in place of fine‐scale FEM for areas where there are lower
gradients (e.g. more than 4 layers below the melt pool)
Banded vectorization Solver
Called for when solving sparse FEM problems

21. Eigensolver
• Strategy
– Compute 3-4 layers using 3DSIM Multi-Scale FEA
– Use the Eigensolver when more than 3-4 layers away from the melt pool
• Advantages
– Time to get the SAME ANSWER is orders of magnitude less
• Disadvantages
– Mode computations are hard to derive for new problems, it is only
applicable to physics problems for which we’ve derived eigenmodal
solutions
• Our Eigensolver is tested and works well for thermal and decoupled
stress/strain problems, but we are still testing our approach for the Material
(crystal plasticity) Eigensolver

22. Comparing Thermal
Eigensolver Answers to FEA
0 1000 2000 3000 4000 5000
0
0.2
0.4
0.6
0.8
1
Linearthermalfieldsolution
(normalized)
# of nodal points
Modal Reconstruction
Finite Element Solution
Solution match for each node when comparing 3DSIM FEA against the
3DSIM Eigensolver for a point heat source

23. Banded Vectorization
Number Sorting
Eliminate Meaningless Computation
7 additively
manufactured
layers
Top surface Boundary condition Optimal tolerance FLOPS
point force 1000 7.00%
center line parallel to X axis 2511.8864 4.00%
Line along one of the diagonal 2511.8864 5.00%
Area force 63.09 40.00%

24. Periodic and Higher
Order Boundary
Conditions (PHOBC)
• We have derived and are testing an eigenmodal
approach to:
– Identify Symmetry & 1st to 4th Order Periodicity in
Boundary Conditions BEFORE calculating FEA for a
New Layer
• Calculation is Based upon Prior Layer Histories and the
Scanning Parameters that will be used for upcoming layer
• If periodicity occurs AND a prior answer is
known… then … feed forward the correct answer
into appropriate portions of the layer
– Calculate any unknown answers using FEA

25. Time and Efficiency Comparisons
(assuming 16 teraflops machine)
• Fine Gridding (using ANSYS or similar method) = 5.7 10 years
• ANSYS (with multi-scale) = 8.9 10 years (89 billion years)
• 3DSIM (with multi-scale) = 1.3 10 years (1.3 billion years)
• 3DSIM(…+Z direction Eigenmodes after 4 layers) = 208 years
• 3DSIM(…+Banded vectorization) = 22.1 years
• 3DSIM(…+PHOBC) = 22.1 10 years~0.2 hours
 Typical Desktop Computer will do 3DSIM (…+PHOBC)=166 days
 That’s why we buy $20k-$30k GPU computers…
 US Fastest GPU Computer (TITAN)
 3DSIM (…+Z Direction Eigenmodes)=54 days
 3DSIM (…+Banded vectorization)=6 days
 3DSIM (…+PHOBC)=720 µs

26. What are we working on
Currently?
• Converting all our Matlab and Fortran code into
C++ and C# code to run on a CUDA GPU
• Running sensitivity analyses on each module as it
is developed
• Validating each module against
– Analytical solutions
– Other software tools
– Our software prior to turning on each new module
– Experiments

27. Our Products
• Full-blown “Everything 3DSIM Offers” Products:
– Simulating problem sets for others as consultancy
– Cloud-based solutions on a per-use basis
– Licenses for combined hardware/software platforms
• Specialty Software Tools:
– Distortion prediction and compensation tool
– Optimum support structure tool
– Future machine control software
– …and more…

28. • An accurate “3D Print Preview” is becoming a reality
• We have developed a modeling infrastructure with never-
before-seen modeling efficiencies
– Combines “upgraded” FEA with Eigensolvers to solve for every
point in space within a machine for every time step to achieve
highly accurate solutions
• 3DSIM tools will:
– Provide guidance to machine users on how to best optimize their
existing machines and build layouts
– Enable rapid materials insertion, optimization & qualification
– Provide a prediction of part performance before building a part
– Make possible the design and manufacture of better AM machines
Conclusions &
Significance

29. Questions & Comments?
brent.stucker@3dsim.com
+1-435-363-5197

30. Material
Information
Module
Process
Information
Module
Process
Solver
Thermal History & Residual
Stress/Strain Database
Material
Solver
Dynamic Mesh
Module
Material Database
Euler Angle
Generator
Simplified Block Diagram for
Fine‐Scale “3DSIM” Solution

31. 3DSIM Software has Been Developed
and/or is Being Validated Via the
Following Projects
Involving Both 3DSIM and the University of Louisville
• Development of Distortion Prediction and Compensation Methods for Metal Powder-
Bed AM – America Makes, 2014-2015
• Predicting Residual Stress in Metallic Additive Manufacturing – STFC EU consortium,
2014-2015
• Further Development of 3DSIM Models – DARPA (anticipated) 2014-2015
• Modeling of DMLS Ti6/4 Residual Stress & Supports -- AFRL/MLPC, 2012-2015
Based Research at the University of Louisville
• Modeling of DMLS In625 -- NIST, 2013-2015
• Rapid Qualification of DMLS/EBM Ti6/4 -- America Makes, 2013-2015
• Modeling of DMLS Ti6/4 Arbitrary Powders –AFRL/MLPC, 2013
• Modeling of Friction Stir AM -- NSF, 2012-2015
• Modeling & Closed Loop Control of UC -- ONR, 2011-2014
• Multi-Material UC – ONR, 2007-2011

32. University of Louisville Confidential
Information
Automatic Support Generation Tool
Process
Solver
Support Generation Module
Interacts with Full Bed Solution Module (using
the capabilities of the of Enhanced Contact
Solver, P.H.O.B.C Module and Eigensolvers) to
Quickly Derive Near‐Optimum Solutions for
Support Placement based upon user‐selected
Support Geometry, Material & Process
Parameters. Solution based upon residual
stress/strain information and may require
continuum equations derived from full‐
factorial simulations.
Process
Information
Module
Full Bed
Solution
Module
Material
Information
Module
Support
Generation
User Interface
Ability to graphically
interact with part
orientation, support
strategy, etc. and
visually see the effects
of changes on residual
stress/strain behavior,
and necessary supports
to meet user‐defined
strain/accuracy results

33. Arbitrary Powders in Metal
Laser Sintering
• Takes simple powder tests as inputs
– Powder density, morphology & chemistry
• Uses empirical relationships to convert powder
tests into important processing variables
– Powder bed absorptivity, thermal conductivity, etc.
• Runs our simulation algorithms near previously
determined “good” operating parameters for a
well-known powder type to find equivalent
“good” parameters for the new powder

34. University of Louisville Confidential
Information
Scan Strategy Simulation Tool
Process
Solver
Scan Strategy Module
Interacts with Fine‐Scale Process/Material
Solvers to parametrically investigate different
scanning strategies based upon user‐selected
Part Geometry, Material, and Process
Parameters. Returns residual stress/strain
information. It could utilize continuum
equations for material properties solved
within the Process Solver very quickly, or for
new materials it may require fine‐scale
material simulations. It should have an
integrated “balling” predictor calculation.
Process
Information
Module
Material
Information
Module
Material
Solver
Scan Strategy
User Interface
Ability to graphically
change scan strategies,
part geometry, etc. and
visually see effects of
changes in strategy on
residual stress/strain
behavior,
microstructure, build
time, etc.

35. Real‐Time Process Monitoring and
Part Qualification Tool
Process Monitoring Module
Use SLI reconstruction and process/material
knowledge to simulate the Full Bed Solution.
Compare the Thermal History, Distortion and
geometry to the simulated results every time‐
step and/or every layer. Store user‐required
information and anomalies as a part history
tool. Use User‐selected metrics to label the
part as “qualified” or questionable
Process
Information
Module
Material
Information
Module
Process
Monitoring
User Interface
Ability to graphically
see, layer‐by‐layer,
comparisons of
simulation and sensor
data. User‐selectable
quality metrics and
comparison metrics
drive long‐term
database storage
decisions.
Thermal,
Distortion
& Optical
Sensors
Full Bed Solution
Module
Probably without the
Material Solver

36. Approach for Polymers
• Find and derive algorithms for the Material &
Process Solvers
– Mathematical relationships which correlate thermal
history to % crystallinity, spherulite morphology, chain
entanglement, molecular weight, % porosity, etc.
– Correlate microstructural features to mechanical
properties mathematically and via experiments

37. Our Estimation Method
• Total # of time steps=50 hours=
∗
1.8 10
• Total Number of Layers ( )= 10
• Time step/layer
Total # of time steps
1.8 10
• Total number of thermal degrees of freedom in a
layer( )= 4 10

38. Theoretical Computational
complexity (in flops)
• Uses Forward substitution for complexity (This is the
expensive term backward is one order less.)
• # of flops= ∑ =
• Since N>>>1, N~N+1
• # of flops=
• Flop Speed per second=F
• Total time=