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  1. 1. Simulation of Additive Manufacturing Technologies: Enabling a 3D “Print Preview” Brent Stucker, PhD Founder & CEO 3DSIM, LLC
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 15. Effect of Powder Packing Density on Melt Pool Geometry (10%, 20%, 30%, 40%, 50%, 60%)
  16. 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. 17. Validation of Mechanical Property Predictions Ti64 Part Behavior-Tensile (EBM processing)
  18. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 29. Questions & Comments? +1-435-363-5197
  30. 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. 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. 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. 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. 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. 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. 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. 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. 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=