Advanced Modeling for Additive Manufacturing

Brent StuckerChief Executive Officer

Professor & Edward R. Clark Chair of Computer Aided Engineering

Department of Industrial Engineering, University of Louisville

3DSIM VISION…

Moving from empirically-driven to simulation-driven Additive Manufacturing

• 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…

“Supports” in 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 buildgive different distortions

Microstructural Variations in Ti6/4

• Less residual stress in Vertical samples columnar grains

• High residual stress in Horizontal samples martensitic streaks

• Identical process parameters for identical partsin an identical layer, in the same build, for the same material, but in different orientations and locations, result indifferent microstructures and

propertiesNear the Bottom of aHorizontal Sample

Near the Bottom of aVertical Sample

The Current Situation for Modeling/Simulation

• We want to know how varying process parameters, powder, geometry, machine type, part location/orientation, etc. affect thermal history, to predict:– Geometric Accuracy

• Including Distortion and Where we Need Supports

– Internal Microstructures

– Properties & Performance

• 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

– Small scale simulations to help us understand some of the physics

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

Problem Complexity

ASSUME

• 40mm x 5mm x 2mm part

– 50mm x 10mm x 10mm mesh

– 30 micron layers

– 10 micron fine mesh box

– 10 micron stepover per timestep

• On a desktop computer

– ANSYS multi-scale moving mesh would take ~150 years to solve

– Solvable in ~ 1 day using 3DSIM optimized C++ FEA tool

• DARPA-Funded Enhancements should reduce time to ~1_minute

THE CORRECT ANSWER REQUIRES VECTOR-BY-VECTOR COMPUTATION

With supportsLayers: 233Hatches Considered: 61,745Laser Positions: 25,766,422

Without SupportsLayers: 66Hatches Considered: 17,490Laser Positions: 13,216,038

• 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

Our Overall Approach

InputsMaterial & Process

Information

Process Structure Solver

3DSIMProcess Solver

(a.k.a FFDAMRD)Multi-scale Finite Element Analysis using Novel Meshing and Efficient

Numerical Techniques)

OutputsThermal History, Cooling Rates, Phase

Information, Residual Stress/Strain

Benefits of our Dynamic Meshing Strategy

• Multi-scale FEA is ~700x faster than using a uniform fine-scale mesh per layer for a full-bed simulation

• 3DSIM is currently ~3000x faster than commercial FEA code for an identical dynamic mesh • Traditional FEA requires matrix

assembly every time-step• 3DSIM “intelligent assembly” of

matrices requires no recalculation of nodal connectivity

• Thus, we are ~2 million times faster than typical approaches

Top Surface Domain in the x direction

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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

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0.2

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0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10

-3

500

1000

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Unstable thermal contours at turns

Stable thermalcontours

ScanStrategy

• For metals, the lack of symmetry and complex thermal histories mean you cannot create representative solutions

• The correct answer requires the correct scan vectors

Simulation Results: Complex Thermal History and Comparison of Simulation with Experiments

“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

Supplier Laser Power (W)

Scan Speed (mm/s)

Layer Thickness (μm)

Hatch Spacing (μm)

EOS 186 1200 30 77LPW 186 1200 30 86

Raymor 186 1200 30 100

– Powder Bed Density & Powder Bed ThermalConductivity inputs were changed based uponexperiments

– Hatch spacing was varied to achieve the samepredicted melt pool overlap for EOS, LPW and Raymorpowders

Validation Experiments

EOS Ti64 Microstructure

LPW Ti64 Microstructure

Raymor Ti64 Microstructure

• Near full density parts and similar microstructures

– We can predict parameters to match melt pool depth towidth ratios or any other phenomena to make sure a newpowder acts like your old powder…

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 × 1018years

– Just including the matrix multiplication flops, nothing else…

• ANSYS (with multi-scale) ~8.9 × 1010years (89 billion years)

• 3DSIM (with multi-scale) ~3 × 107years (30 million years)

• 3DSIM(…+Z direction Eigenmodes after 4 layers) ~200 years

• 3DSIM(…+Intelligent Cholesky) ~15 years– Don’t multiply the zeros in the matrices

• 3DSIM(…+PHOBC) = 22.1× 10−6 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

• Doesn’t include pre-processing, data handling, etc., so it will take more time in real-life…

17

COMSOL

SYSWELD

Thermal “wave” propagation

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

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

3DSIM Competitive Advantages

• Our Value Proposition– Speed

• 103 to 1011+ depending upon the strategy used

– Ease of Use• Intuitive Interfaces

– Better Science• Non-linear material properties & real machine physics

– User-Directed Optimization • Investigate whatever is of interest to the user

– Cloud-Enabled, Modular and Composable• Simulation can be called by other software tools

Conclusions

• An Accurate “3D Print Preview” is Becoming a Reality

• We have developed modeling algorithms with never-before-seen modeling efficiencies– Combines Dynamic FEA with Eigensolvers, Intelligent Cholesky and Periodicity

Inventions

• 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 have begun working with key partners to provide software simulations as a service and we plan to offer software licenses to beta customers starting in early 2015.

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 ($3+million in R&D to date)

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

History of 3DSIM

Technology Development

• Simulation of ultrasonic consolidation began in 2009 – Dislocation Density based

Crystal Plasticity Finite Element Modeling

• Simulation of metal laser sintering began in 2010– Dynamic meshing

infrastructure

Business Development

• Formed 3DSIM in late 2013

• Exclusive contract signed with UofL early 2014

• Commenced operations March 2014

• Completed Dynamic Mesh FEA implementation Sept. 2014– Started offering simulation

services

• Support Optimization tool targeted for Q2 2015