Process Model for Metal Additive Manufacturing

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NAFEMS NORDIC "Exploring the Design Freedom of Additive Manufacturing

1 VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD Process Model for Metal Additive Manufacturing Tom Andersson, Anssi Laukkanen, Tatu Pinomaa NAFEMS NORDIC "Exploring the Design Freedom of Additive Manufacturing through Simulation" Kalastajatorppa 2 A PROCESS SIMULATION I METALS

2 Contents Multiscale modeling for metal additive manufacturing: concepts and ICME toolsets Thermomechanical finite elements based process model and machine integration Case analysis results & examples Evaluation of material properties & performance 12/12/2016 2

3 Multiscale modeling for metal additive manufacturing Powder and alloy design Material property & performance design Modeling material structure properties and performance Discrete modeling of powder bed physics Microstructural FEM to predict materials (and component) properties, behavior and performance SLM process design and optimization Powder bed model for heat transfer and solidification Thermodynamics and phase fields Thermomechanical modeling of selective laser melting PRODUCT PERFORMANCE AND COST Part geometry design Topology optimization PF model for reactive wetting, phase and microstructure prediction Part specific optimized process design Possible redo of shape optimization to better design the production phase 12/12/ support structure

Model Generation & I/O with SLM Machine Model creation via a push of a button

create the thermomechanical process model directly for simulation with a

scan strategy from CLI Currently with SLM125, integration via log and build

Geometry parsed from bracket,example layers 1 & 100 Reproduce scan strategy

4 Model Generation & I/O with SLM Machine Model creation via a push of a button : 1) interface to read and process machine build files (and logs) and 2) create the thermomechanical process model directly for simulation with a solver of interest (presently Abaqus, code_aster, some OS libraries) Parsing scan strategy from CLI Currently with SLM125, integration via log and build files (CLIs++). Geometry parsed from bracket,example layers 1 & 100 Reproduce scan strategy 1-1 with the SLM125 machine 12/12/2016 Extensible since the basis classes describing the AM build process have been developed and established for the SLM125 5

5 1. Scan strategy and process parameters parsed from cli & system configuration and log files 2. Re-construct scanning strategy and process parameters, transfer via user subroutines and databases to finite element model. 3. Model definition + heat source via separate library (meshing, heat transfer, properties etc.) 4. Material model definition via separately library (thermal and mechanical models) 5. Solution using either implicit or explicit approaches 6. Post-processing for engineering material properties 12/12/2016 6

SLM transient thermal process model The process model consists of the powder bed, laser heat source, and the respective thermal

Laser heat source Beam described as a moving Gaussian surface heat flux Base plate at uniform temperature or convection to a

Adaptive mesh refinement and coarsening.

6 SLM transient thermal process model The process model consists of the powder bed, laser heat source, and the respective thermal initial and boundary conditions Convection BC via top surface As output thermomechanical history, residual stresses, strains etc. Laser heat source Beam described as a moving Gaussian surface heat flux Base plate at uniform temperature or convection to a thermal sink Convection BC to powder bed sides Derive a section of powder bed and update its size as required. Adaptive mesh refinement and coarsening. Transient thermal solution utilizing a moving mesh motif over the respective process history and layers of interest. Powder vs solid properties following common conventions, using packing density to homogenize properties. 12/12/2016 7

7 Kinetic model for diffusion affiliated phase transformations Nucleation governed by ΔG for precipitation acting as a driving force. Nucleation rate via Berker-Döring, including free energy reduction due to phase transformation and free energy increase due to interface between phases. Growth via the Zener equation. If cooling rate over threshold time independent transformation to martensite (CCT like tracking) Otherwise, time dependent nucleation and growth process. Tweaking or calibration/validation using CCT curves, but predictive. Enables tracking of local powder & solid material state on the basis of thermal history. In the future considered a template for coarse graining smaller resolution, e.g. phase field simulation, results. Thermodynamical model based on classical nucleation and growth theories to address precipitation of metastable and stable phases. 1 st Nucleation and growth, followed 2 nd by growth and coarsening (model layout following Deschamps & Brechet, 1999). Example of isothermal transformation in H13 steel: 12/12/ Growth and size of precipitates Solute concentrations

Linking between local thermal solution, process parameters, scan strategy and part features Laser power P = 100 W, beam velocity v =

8 SLM transient thermal process model, some layers of a bracket geometry (~20) Bracket geometry of this case study, approx. 2k layers in experimental build. Temperature isosurfaces, example from layer 1100, 20 by 20 cm powder bed in model. Linking between local thermal solution, process parameters, scan strategy and part features Laser power P = 100 W, beam velocity v = 1000 mm/s Laser power P = 150 W, beam velocity v = 1000 mm/s Laser power P = 250 W, beam velocity v = 1000 mm/s Laser power P = 375 W, beam velocity v = 1000 mm/s 12/12/2016 9

SLM transient thermal process model, test samples (thermal field, layer 20) Sample No. Scanning speed mm/s Power W VED J/mm 3 Measured porosity (%) Calculated porosity (%) (simulation model) 2 666.

9 SLM transient thermal process model, test samples (thermal field, layer 20) Sample No. Scanning speed mm/s Power W VED J/mm 3 Measured porosity (%) Calculated porosity (%) (simulation model) Process parameters, measured porosities and calculated porosities of five DOE samples from second sample set Modeling porosity evolution during metal AM process 12/12/

Image based microstructural modeling of fatigue crack initiation

One example of defect structure porosity 1 st principal stress Four

inclusion porosity microstructure Equivalent plastic strain Multiple

amplitude strain amplitude Derivation of time to initiate short

microstructures (precipitate hardened steel).

10 Image based microstructural modeling of fatigue crack initiation Microstructure founded computation of cycles to short crack initiation One example of defect structure porosity 1 st principal stress Four different microstructures, fatigue performance indicator large inclusion porosity microstructure Equivalent plastic strain Multiple small defect types (inclusions, porosity) Crack-like defects strain amplitude strain amplitude Derivation of time to initiate short fatigue cracks on the basis of microstructural models of SLM microstructures (precipitate hardened steel). The defects which completely deteriorate the fatigue performance of the microstructure are high aspect ratio cracks. 12/12/

Image based microstructural modeling of fatigue crack initiation FS parameter has been demonstrated to correlate to multiaxial fatigue crack initiation results both in high and low cycle fatigue (in

11 Image based microstructural modeling of fatigue crack initiation FS parameter has been demonstrated to correlate to multiaxial fatigue crack initiation results both in high and low cycle fatigue (in shear dominated crack initiation) P FS = γ p max 2 p 1 + K σ n max σ Y is the maximum cyclic plastic shear strain over a finite volume of material,k γ max incorporates the influence of normal stress,σ n max is the maximum stress normal to the plane of p and σ Y is the cyclic yield strength. γ max For estimation of cycles to initiation, the FS parameter is linked to the Coffin- Manson (CM) strain life P FS = γ f 2 N i c, γ f is strain affiliated coefficient application to crack initiation at the scale of interest, N i the cycles to initiation and c is the CM exponent. Performance gains in product lifetime, interpretation of defect significance Samples 2, 3, 4, 11, 24 Modeling porosity evolution during metal AM process Modeling subsequent product performance (with respect to fatigue) 12/12/

Defect density for different computed process Experimental example parameter

porosity Critical components Non-load critical components Melt pool

damaged material (pores, cracks, delaminations, gas pores, ).

12 Defect density for different computed process Experimental example parameter sets Velocity [mm/s] Raw data, 6 by 6 points for a property surface Cracks, porosity Critical components Non-load critical components Melt pool instabilities Perfect solid Metallic foam Power [W] Defect density = ratio of damaged material (pores, cracks, delaminations, gas pores, ). Defect density = 1 ( perfect solid ), Defect 12/12/2016 density = 0 completely porous/damaged material 14

The approach enables the definition of a machine realistic model of the metal AM build process. The model outcome can be exploited in evaluation of engi

13 Summary and conclusions Framework for modeling metal AM processes and enabling streamlined model generation has been presented. The toolset provides means to derive complex models with ease as well as modify them e.g. to run DoE on the model. The approach enables the definition of a machine realistic model of the metal AM build process. The model outcome can be exploited in evaluation of engineering material properties as well as e.g. analysing in more detail part performance with respect to fatigue. H-adaptivity and mesh refining/coarsening is to be further developed to decrease computing times and increase model sizes. Improve links with other tools to improve physical descriptivines, e.g. solidification and powder bed models to enhance and mitigate limitations of thermomechanical finite elements. 12/12/

Computational Materials Design. Tomi Suhonen, Anssi Laukkanen, Matti Lindroos, Tom Andersson, Tatu Pinomaa, et al. VTT Materials & Manufacturing

Computational Materials Design Tomi Suhonen, Anssi Laukkanen, Matti Lindroos, Tom Andersson, Tatu Pinomaa, et al. VTT Materials & Manufacturing Contents Click to edit Master title style Computational materials