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"

22.11.2016 Kalastajatorppa

2 A – PROCESS SIMULATION I – METALS

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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

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PRODUCT PERFORMANCE

AND COST

Multiscale modeling for metal

additive manufacturing

Discrete modeling of

powder bed physics

Thermodynamics and

phase fields

Modeling material structure → properties and performance

Topology optimization

Thermomechanical modeling of

selective laser melting

→ Part specific optimized process

design

SLM process design and optimization

Powder and alloy design

Material property & performance design

Part geometry design

Microstructural FEM to predict materials (and

component) properties, behavior and performance

Powder bed model for

heat transfer and

solidification

Possible redo of shape

optimization to better design

the production phase

support structure

PF model for reactive wetting,

phase and microstructure prediction

512/12/2016

Model Generation &

I/O with SLM Machine

Currently with

SLM125, integration

via log and build files

(CLIs++).

Parsing scan strategy from CLIReproduce scan strategy 1-1 with the SLM125 machine

Extensible since the basis classes describing the AM build process have been developed and established

for the SLM125

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)

Geometry parsed from bracket ,example

layers 1 & 100

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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

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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 to

powder bed sides

Convection BC via

top surface

Base plate at uniform

temperature or convection to a

thermal sink

Laser heat source

Beam described as a

moving Gaussian

surface heat flux

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.

As output

thermomechanical

history, residual

stresses, strains

etc.

Derive a section of powder bed and

update its size as required. Adaptive

mesh refinement and coarsening.

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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. 1st Nucleation and growth, followed 2nd by

growth and coarsening (model layout following Deschamps &

Brechet, 1999). Example of isothermal transformation in H13

steel:

Growth and size of precipitates

Solute concentrations

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SLM transient thermal process model, some

layers of a bracket geometry (~20)

Temperature isosurfaces, example from layer 1100,

20 by 20 cm powder bed in model.

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

Bracket geometry of this

case study, approx. 2k

layers in experimental build.Linking between local thermal

solution, process parameters,

scan strategy and part features

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SLM transient thermal process model, test

samples (thermal field, layer 20)

Modeling porosity evolution during metal AM

process

Sample

No.

Scanning speed

mm/s

Power

W

VED

J/mm3

Measured

porosity (%)

Calculated porosity (%)

(simulation model)2 666.7 100 50 12.74 14.73 1200 180 50 2.67 2.74 1200 228.51 63.5 0.23 0.611 1200 200 83.3 0.15 0.424 993.7 250.51 84 0.09 0.08

Process parameters, measured porosities and

calculated porosities of five DOE samples

from second sample set

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Microstructure founded computation of cycles to short crack initiation

Image based microstructural modeling of

fatigue crack initiation

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.

strain

amplitudestrain

amplitude

microstructure

porosity

porositylarge

inclusion

One example

of defect

structure

Multiple small defect

types (inclusions,

porosity)

Crack-like

defects

1st principal stress

Equivalent plastic strain

Four different microstructures, fatigue

performance indicator

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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)

𝑃𝐹𝑆 =∆𝛾𝑚𝑎𝑥

𝑝∗

21 + 𝐾′ 𝜎𝑚𝑎𝑥

𝑛∗

𝜎𝑌

∆𝛾𝑚𝑎𝑥𝑝∗

is the maximum cyclic plastic shear strain over a finite volume of material,𝐾′

incorporates the influence of normal stress,𝜎𝑚𝑎𝑥𝑛∗ is the maximum stress normal to the plane of

∆𝛾𝑚𝑎𝑥𝑝∗

and 𝜎𝑌is the cyclic yield strength.

For estimation of cycles to initiation, the FS parameter is linked to the Coffin-

Manson (CM) strain life

𝑃𝐹𝑆 = 𝛾𝑓′ 2 𝑁𝑖

𝑐 ,

𝛾𝑓′ is strain affiliated coefficient application to crack initiation at the scale of interest, 𝑁𝑖 the

cycles to initiation and 𝑐 is the CM exponent.

Modeling porosity evolution during metal AM

process Modeling subsequent product performance (with

respect to fatigue)

Performance gains in product lifetime, interpretation of

defect significance

Samples

2, 3, 4,

11, 24

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Raw data, 6 by 6 points for a

property surface

Power [W]

Velocity

[mm/s]

Melt pool

instabilities

Defect density = ratio of damaged material (pores, cracks,

delaminations, gas pores,…).

Defect density = 1 (“perfect solid”),

Defect density = 0 completely porous/damaged material

“Metallic foam”

“Perfect solid”

Non-load critical

components

Critical components

Cracks,

porosity

Experimental example

Defect density for different computed process

parameter sets

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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.