Recent Trends in Additive Manufacturing & the Need for Predictive Simulation. Brent Stucker, Ph.D. Chief Executive Officer

Transcription

1 Recent Trends in Additive Manufacturing & the Need for Predictive Simulation Brent Stucker, Ph.D. Chief Executive Officer

2 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

3 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 lightactivated polymerization.

4 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

5 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.

6 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 $2k than industrial machines for $10k $200k (400+ manufacturers?) Renewed interest in manufacturing parts via extrusion High temp materials, concrete, fiberreinforced composites, etc. Big Area Additive Manufacturing at ORNL

7 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

8 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

9 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

10 Engineering Implications MAIN DRIVING FORCE FOR IMPLEMENTATION More Complex Geometries Parts Consolidation Designed internal structures

11 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

12 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

13 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

14 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

15 AM can now enable us to control geometry Butthe weoverall don t know how to: of a part, which could be represent this typeeach of multi-scale made up ofefficiently a truss network, where truss has an a CAD environment, or optimized geometry thicknessinand could have an individually Efficiently optimize these multi-scale features, or controllable microstructure Efficiently simulate or thematerial. link between AM process parameters and microstructure, or Efficiently compute the effects of changes in microstructure on part performance Courtesy David Rosen, Georgia Tech

16 3DSIM VISION Moving from empirically driven to simulation driven Additive Manufacturing

17 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

18 Metals use Complex Scan Strategies Metal Laser Sintering EBM

19 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

20 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

21 Problem Complexity THE CORRECT ANSWER REQUIRES VECTOR BY VECTOR COMPUTATION Without Supports Layers: 66 Hatches Considered: 17,490 Laser Positions: 13,216,038 40mm x 5mm x 2mm part With pillar supports Layers: 233 Hatches Considered: 61,745 Laser Positions: 25,766,422 ANSYS Computational Time ~150 years

22 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

23 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

24 Inputs Material & Process Information Process Structure Solver 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

25 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

26 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 Top Surface Domain in the y direction Unstable thermal contours at turns Stable thermal contours Top Surface Domain in the x direction x For metals, the lack of symmetry and complex thermal histories mean you cannot create representative solutions The correct answer requires the correct scan vectors

27 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

28 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) Scan Speed (mm/s) Layer Thickness (μm) Hatch Spacing (μm) EOS LPW Raymor

29 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

30 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) 10 8 elements per layer, elements per build if fine meshed everywhere 10 microseconds time steps to capture melting 50 hours of actual laser scan time total time steps

31 Time and Efficiency Comparisons (assuming 16 teraflops machine) Fine Gridding (using ANSYS or similar method) = years Just including the matrix multiplication flops, nothing else ANSYS (with multi scale) ~ 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 Don t multiply by the zeros in the matrices 3DSIM( +PHOBC) = years ~ 0.2 hours SYSWELD COMSOL Thermal wave propagation Mathematically identify symmetry & periodicity (0 th, 1 st, 2 nd, 3 rd & 4 th Order) Eliminates errors associated with building a representative database and then trying to apply to a new machine, material or process parameter combination 31

32 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)

33 Validation of Mechanical Property Predictions Ti64 Tensile Behavior (EBM processing)

34 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

35 User Interface

36 Material Interface

37 Scan and Simulation Options Or Import all of the Prior Information from the Machine Software And Then Press RUN

38 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

39 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 A Beta program is planned for 2016 for our suite of tools

40 Questions & Comments?

41 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 Predicting Residual Stress in Metallic Additive Manufacturing STFC EU consortium, (anticipated) Further Development of 3DSIM Models DARPA Modeling of DMLS Ti6/4 Residual Stress & Supports AFRL/MLPC, Basic Research at the University of Louisville Modeling of DMLS In625 NIST, Rapid Qualification of DMLS/EBM Ti6/4 America Makes, Modeling of DMLS Ti6/4 Arbitrary Powders AFRL/MLPC, 2013 Modeling of Friction Stir AM NSF, Acoustic Resonance Techniques for Qualification of Metal AM ONR Modeling & Closed Loop Control of Ultrasonic Consolidation ONR, Multi Material Ultrasonic Consolidation ONR,

42 Flow Diagram for Complete 3DSIM Solution Process Information Module Material Information Module Dynamic Mesh Module Microscopy AND/OR Euler Angle Generator Process Solver Material Solver Thermal History & Residual Stress/Strain Database Material Property Database

43 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)

44 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.)

45 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. Solidification front Melting phase Keyhole front Melting phase Keyhole Melting front void Melt pool bottom Solid phase Solid phase 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) µm