1. business and industrial
2. manufacturing

Recent Trends in Additive Manufacturing & the Need for Predictive Simulation
Brent Stucker, Ph.D.
Chief Executive Officer
What is Additive Manufacturing? • According to ASTM F42 and ISO TC261
– The process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies
Key Developments in Vat Photopolymerization
• Expiration of initial stereolithography
patents are opening up the marketplace
– Increased proliferation of projector‐based (DLP/LCD/LED) technology which cure entire layers at once. • An additive manufacturing process in which liquid photopolymer in a vat is selectively cured by light‐
activated polymerization. Key Developments in Material Jetting
• New Stratasys/Objet Connex 500
– Multi‐material & Multi‐color
• Many traditional “2D printing” companies are investigating 3D printing
– HP MultiJet Fusion
– Metals are starting to be publically discussed
• Significant interest in printed electronics
– At the intersection between 2½D & 3D geometries
• An additive manufacturing process in which droplets of build material are selectively deposited
Key Developments in Binder Jetting
• 3D Systems marketing “Colorjet” (based on Zcorp)
– Printing sugary food and ceramics (pottery & art)
– Announced a color personal 3D printer
• ExOne is expanding “sand printing” and builds metal parts for Shapeways
• Voxeljet, and others make marketplace dynamic
– Continuous build platform design has interesting ramifications
• An additive manufacturing process in which a liquid bonding agent is selectively deposited to join powder materials. Key Developments in Material Extrusion
• An additive manufacturing process in which material is selectively dispensed through a nozzle or orifice
•
Expiration of patents has led to a proliferation of 3D printers
– More “personal” machines sold @$500‐$2k than “industrial” machines for $10k‐$200k • (400+ manufacturers?)
•
Renewed interest in “manufacturing” parts via extrusion
– High‐temp materials, concrete, fiber‐
reinforced composites, etc.
– Big Area Additive Manufacturing at ORNL
Key Developments in Directed Energy Deposition
• Electron Beam with wire seems to be leading for part production currently – DARPA Open Mfg w/ Sciaky
• DoD is interested in laser powder deposition for repair (America Makes project)
– Manufacturers are marketing laser deposition heads as add‐
ons to existing machine tools
• An additive manufacturing process in which focused thermal energy is used to fuse materials by melting as they are being deposited
Powder Bed Fusion
• An additive manufacturing process in which thermal energy selectively fuses regions of a powder bed
– SLS, SLM, DMLS, EBM, BluePrinter, etc. – Polymers, metals & ceramics
Key Developments in Powder Bed Fusion
• The most‐used platform for “functional parts” • Significant R&D investments
• Many metal machine manufacturers
– Arcam, SLM Solutions, ConceptLaser, EOS, 3DSystems/Phenix, Renishaw, Realizer
• Many new metal laser sintering machines coming
– Additive Industries, MatterFab, Velo3D, Japanese & Chinese Mfg, etc.
• Starting to see new polymer machine manufacturers
– We just took delivery this year of a new machine
• Open versus Closed machine architecture battles
• GE’s purchase of Morris Technologies (2012) is still having major ramifications on the metal laser sintering marketplace
Engineering Implications
MAIN DRIVING FORCE FOR IMPLEMENTATION
• More Complex Geometries
– Parts Consolidation
– Designed internal structures
Business Trend
• Removes much of the low‐cost labor advantage
• Many new business models
– People can print parts at home, at a local print shop (Staples), or on‐line (shapeways.com)
– New Machines
• Patents Expiring
– Software tools
– Service providers
Pharmaceutical Manufacturing in China
Key Trends Discussion Points
• 2 Main “Conglomerates” forming
– 3D Systems
– Stratasys
• Many market entrants
– Many start‐ups
• Personal/Consumer Printing • Metal AM
– Lots of Venture Capital Money
• Many uninformed investors
• A few very informed investors
– Scams are occurring
Key Trends
• Governments are becoming increasingly involved
– America Makes
– EU activities
• Including individual states such as UK, Germany
– China is investing hundreds of millions
– Singapore
– Japan
Key Trends
• People are focusing on the main obstacles to commercial/industrial implementation – Repeatability of the process
– Reproducibility between machines
– Quality of the product for a particular use
• Accuracy, Surface Finish, Porosity, Mechanical Properties, etc.
– Speed
AM can now enable us to…
…control
• Butthe overall geometry
we don’t know how to: of a part, which could be • Efficiently represent this type of multi-scale
made up of a truss network, where each truss has an geometry in a CAD environment, or
optimized thickness and could have an individually • Efficiently optimize these multi-scale features, or
controllable microstructure or material.
• Efficiently simulate the link between AM
process parameters and microstructure, or
• Efficiently compute the effects of changes in
microstructure on part performance
Courtesy David Rosen, Georgia Tech
3DSIM VISION…
Moving from empirically‐driven to simulation‐driven Additive Manufacturing
“Supports” in Metal Laser Sintering
• Supports are placed based upon geometry and user experience
– No calculation of stress
– Extra supports increases post‐processing costs – Supports can ruin features
– Under‐supported regions cause blade crashes
• ~$100k/year/machine…
Metals use Complex Scan Strategies
EBM
Metal Laser Sintering
Typical Process Variation Effects
• 2 mm wall made from Inconel 625
– XZ section showing effects of scan pattern on microstructure
• Identical geometries in the same build
give different distortions
Microstructural Variations in Ti6/4
• Less residual stress in Vertical samples 
columnar grains
• High residual stress in Horizontal samples 
martensitic streaks
Near the Bottom of a
Vertical Sample
Near the Bottom of a
Horizontal Sample
• 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
Problem Complexity
THE CORRECT ANSWER REQUIRES VECTOR‐BY‐VECTOR COMPUTATION
Without Supports
Layers: 66
Hatches Considered: 17,490
Laser Positions: 13,216,038
With pillar supports
Layers: 233
Hatches Considered: 61,745
Laser Positions: 25,766,422
ANSYS Computational Time ~150 years
40mm x 5mm x 2mm part
What’s Wrong with Existing Simulation Approaches?
• They’re too slow…
– They can’t efficiently solve problems which have dynamically changing geometry and moving energy sources
• Especially multi‐physics, multi‐scale modeling – They aren’t optimized for GPUs.
• They’re not user‐friendly
– They require expert users – Many different software tools/approaches are required to solve simple problems
– Not designed for additive manufacturing
• No process or material‐specific information for AM
Our Overall Approach
• 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
Inputs
Process Structure Solver
Material & Process Information
3DSIM
Process Solver
(a.k.a FFDAMRD)
Multi‐scale Finite Element Analysis using Novel Meshing and Efficient Numerical Techniques)
Outputs
Thermal History, Cooling Rates, Phase Information, Residual Stress/Strain
Benefits of our Dynamic Meshing Strategy
• 3DSIM is currently ~3000x faster than commercial FEA code for an identical dynamic mesh • Traditional FEA requires matrix assembly every time meshing changes
• 3DSIM “intelligent assembly” of matrices requires no recalculation of nodal connectivity
Simulation Results: Complex Thermal History and Comparison of Simulation with Experiments
2
x 10
-3
Thermal contours at arbitary time steps during 1st layer of Laser scanning
Scan
Strategy
1.8
Top Surface D om ain in the y direction
5000
4500
1.6
4000
1.4
3500
1.2
3000
Stable thermal
contours
1
2500
0.8
2000
0.6
1500
0.4
1000
0.2
0
0
Unstable thermal contours at turns
0.2
0.4
0.6
0.8
1
1.2
1.4
Top Surface Domain in the x direction
500
1.6
1.8
2
x 10
-3
• For metals, the lack of symmetry and complex thermal histories mean you cannot create representative solutions
• The correct answer requires the correct scan vectors
“New Powder Insertion” Case Study
• Three different powders from different suppliers were tested and simulated
EOS spherical LPW spherical Raymor significantly more agglomerated fine particles
Model Guided Processing Parameter Predictions
– Powder Bed Density & Powder Bed Thermal
Conductivity inputs were changed based upon
experiments
– Hatch spacing was varied to achieve the same
predicted melt pool overlap for EOS, LPW and Raymor
powders
Supplier Laser Power (W)
EOS
186
LPW
186
Raymor
186
Scan Speed (mm/s)
1200
1200
1200
Layer Thickness (μm)
30
30
30
Hatch Spacing (μm)
77
86
100
Validation Experiments
• Near full density parts and similar microstructures
– We can predict parameters to match melt pool depth to
width ratios or any other phenomena to make sure a new
powder acts like your old powder…
EOS Ti64 Microstructure
LPW Ti64 Microstructure
Raymor Ti64 Microstructure
How Large is the Full‐Bed Problem We Want to Model?
• Finite Element Modeling of a commercial full‐scale build:
– 200mmx200mmx200mm powder bed size
– 10 micron resolution small‐scale mesh (2 elements/layer)
• 108 elements per layer, 1012 elements per build if fine meshed everywhere
– 10 microseconds time steps to capture melting
– 50 hours of actual laser scan time
• 1010 total time steps
Time and Efficiency Comparisons (assuming 16 teraflops machine)
•
Fine Gridding (using ANSYS or similar method) = 5.7
10 years
– Just including the matrix multiplication flops, nothing else…
•
•
•
•
ANSYS (with multi‐scale) ~8.9 10 years (89 billion years)
3DSIM (with multi‐scale) ~3 10 years (30 million years)
3DSIM(…+Z direction Eigenmodes after 4 layers) ~200 years
3DSIM(…+Intelligent Cholesky) ~15 years
SYSWELD
COMSOL
Thermal “wave” propagation
– Don’t multiply by the zeros in the matrices
•
3DSIM(…+PHOBC) = 22.1 10
years ~ 0.2 hours
– Mathematically identify symmetry & periodicity (0th, 1st, 2nd, 3rd & 4th Order)
– Eliminates errors associated with building a “representative database” and then trying to apply to a new machine, material or process parameter combination
31
Inputs
Crystal structure (Euler angles & dislocation density), thermal history and mechanical loading information (e.g. tensile/fatigue test).
Structure Properties
Solver
3DSIM
Material Solver
Multi‐scale Dislocation Density based Crystal Plasticity Finite Element Solver
Outputs
Dislocation Density history, stress/strain curves, slip details, Modified Microstructure (grain size, orientation, etc)
Validation of Mechanical Property Predictions
Ti64 Tensile Behavior (EBM processing)
Other Unique Aspects of our Code
• Thermal eigenmodes appear to correspond to microstructural texture
– Phase changes are already in the code, but not texture & grain size
• Residual Stress is calculated in parallel to thermal history at a negligible additional computational cost
– Residual stress takes significantly more time to compute using traditional FEA
• We are currently implementing the ability to predict:
– Meltpool instabilities which lead to balling
– Multiple porosity mechanisms, including keyhole porosity
– Surface Roughness
User Interface
Material Interface
Scan and Simulation Options
Or Import all of the Prior Information from the Machine Software
And Then Press “RUN”
But… Are these codes Trustworthy?
• We have tested each Matlab/Fortran algorithm • Three programs will test 3DSIM’s code
– GE, UTRC & Honeywell as an America Makes Project
• Distortion Prediction & Compensation
• Multiple machines and multiple materials
– Rutherford Appleton Labs and Aerospace Companies
• Residual Stress Predictions & Neutron Beam Validation
– DARPA Open Manufacturing Program
• Testing full‐scale simulation capabilities
Conclusions
• An Accurate Prediction of Metal AM is Becoming a Reality
• We have developed modeling algorithms with never‐before‐seen modeling efficiencies
• 3DSIM tools will:
– Provide guidance to machine users on how to best optimize their existing machines, build layouts, and supports
– 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
– Make possible the design and manufacture of better parts
• We currently provide software simulations as a service • We plan to offer support optimization tools in mid 2015.
• A Beta program is planned for 2016 for our suite of tools
Questions & Comments?
brent.stucker@3dsim.com
+1‐435‐363‐5197
Acknowledgements: 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, GE, UTRC & Honeywell 2014‐2015
• Predicting Residual Stress in Metallic Additive Manufacturing – STFC EU consortium, (anticipated) 2014‐2015
• Further Development of 3DSIM Models – DARPA 2014‐2015
• Modeling of DMLS Ti6/4 Residual Stress & Supports – AFRL/MLPC, 2012‐2015
Basic 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
• Acoustic Resonance Techniques for Qualification of Metal AM – ONR 2014‐2017
• Modeling & Closed Loop Control of Ultrasonic Consolidation – ONR, 2011‐2014
• Multi‐Material Ultrasonic Consolidation – ONR, 2007‐2011
Flow Diagram for Complete “3DSIM” Solution
Microscopy
Process Information Module
Dynamic Mesh Module
AND/OR
Euler Angle Generator
Material Information Module
Process Solver
Thermal History & Residual Stress/Strain Database
Material Solver
Material Property Database Process Solver Material Inputs (and their temperature dependencies)
Bulk Material Data –
–
–
–
–
–
–
–
–
–
–
–
Material Chemistry
Absorptivity
Density
Specific Heat
Thermal Conductivity
Solidus Temperature
Coefficient of Thermal Expansion
Elastic Modulus
Poisson’s ratio
Yield Strength
Strain rate sensitivity
Solid State Transition Temperature(s) and phase(s) if present
– If the base plate is different than the powder, then we require base plate properties as well.
Powder Bed Material Data – Absorptivity
• Including energy transmittance through the bed as a function of bed thickness – Packing Density (including anisotropy in x,y,z if known)
– Thermal Conductivity (including anisotropy, if known)
– Coefficient of Thermal Expansion Melt Pool Material Data –
–
–
–
–
–
–
–
Absorptivity
Density
Specific Heat
Thermal Conductivity
Latent Heat of Fusion
Latent Heat of Vaporization
Liquidus Temperature
Vaporization Temperature (of any low‐elemental weight constituents)
Process Solver Machine Information
•
•
Baseplate temperature
Powder bed boundary condition(s) at top and sides
– e.g. constant temperature or a time dependent temperature profile
•
•
•
•
•
•
Partial pressure of oxygen
Temperature dependent convection coefficient at the top of the powder bed
Baseplate thickness
Scan vectors (including order, orientation, hatch spacing, strategy, etc.)
Scan speed (including acceleration and deceleration, if known)
Time between scans
– Delay between hatch lines and pattern‐based delays
•
•
•
Time between layers
Layer thickness
Beam power profile
–
–
–
–
Laser power
Laser wavelength
Beam diameter
Distribution (Gaussian, top‐hat, etc.) Formation of Keyhole Porosity
Keyhole melting is usually unstable, and occurs when the heat input is high. Under the mutual
effects of recoil pressure, surface tension and impacting pressure of melt flow, the keyhole
periodically collapses and breaks into two parts: a reduced keyhole and a void cavity.
Melting
phase
Solidification
front
Melting
phase
Keyhole
front
Melting front
Keyhole
void
Solid
phase
Solid
phase
Melt pool bottom
The periodicity was experimentally verified by a longitudinal cross section of a single bead (LLNL*).
This periodicity and the resulting % porosity is important to predict and validate.
SS 316L, 334W, 171 mm/s
*W.E. King et al. / Journal of Materials Processing Technology 214 (2014) 2915–2925
500µm