Icomp 2016

On the Numerical Simulation of AM Processes M. Chiumenti1,2, M. Cervera1, N. Dialami1, C. Agelet de Saracibar1 1 International Center for Numerical Methods in Engineering (CIMNE) UPC BarcelonaTech, Barcelona, Spain W. Huang2, X. Lin2, L. Wei2, Y. Zheng2, L. Ma2 2 State Key Laboratory of Solidification Processing (SKLSP) Northwestern Polytechnical University (NWPU), Xi’an, China

2nd International Conference on Computational Methods in Manufacturing Processes Liège, Belgium, 18-20 May 2016

ICOMP 2016

Introduction, motivation and goals Problem statement Computational model Numerical simulations Concluding remarks

Outline

Outline

June 19, 2016 Carlos Agelet de Saracibar 2

Introduction, motivation and goals o Additive Manufacturing (AM) o AM Technologies o AM Applications o Key Benefits of AM o Challenges of AM o Goals

Problem statement Computational model Numerical simulations Concluding remarks June 19, 2016 Carlos Agelet de Saracibar 3

Outline > Introduction, Motivation and Goals

Introduction, Motivation and Goals

Additive Manufacturing (AM) “Additive Manufacturing (AM) is a process of joining materials to make objects from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies” (ASTM-F2792-12a)



Additive Manufacturing (AM)

AM Technologies



AM Technologies

AM Powder Bed Fusion (PBF) Directed Energy Deposition (DED)

Raw material Powder Powder, Wire

Energy source Laser, EB Laser, EB, Arc welding

Deposition rate 0.1-0.2 [Kg/h] 0.5-5 [Kg/h]

Layer thickness 0.01-0.1 [mm] 0.25-3.0[mm]

Techniques Selective Laser Sintering (SLS), Selective Laser Melting (SLM), Electron Beam Melting (EBM)

Laser Metal Deposition (LMD)/Laser Engineered Net Shaping (LENS), Shaped Metal Deposition (SMD), Laser Metal Deposition-wire (LMD-w)

Features Complex Shapes, Low Deposition Rates, High Dimensional Accuracy, Small Components

Higher Deposition Rates, Lower Dimensional Accuracy, Larger Components

Applications Tooling, Functional Parts, Prototypes

Metal Part Repair, Add Features to Parts, Tooling, Functional Parts

DED AM Technologies



AM Technologies

DED AM Blown Powder Wire-feed

Raw material Powder Wire

Energy source Laser, EB Laser, EB, Arc welding

Deposition rate 0.5-2.3 [Kg/h] 2.5-5 [Kg/h]

Layer thickness 0.25-1.0 [mm] 1.0-3.0 [mm]

Techniques Laser Metal Deposition (LMD)/Laser Engineered Net Shaping (LENS)

Shaped Metal Deposition (SMD), Hot-wire Gas Tungsten Arc Welding (HW-GTAW), Laser Metal Deposition-wire (LMD-w)

Features High Deposition Rates, Low Dimensional Accuracy, Large Parts

Higher Deposition Rates, Lower Dimensional Accuracy, Larger Parts

Application Metal Part Repair, Add Features to Parts, Tooling, Functional Parts

Metal Part Repair, Add Features to Parts, Tooling, Functional Parts

Powder Bed Fusion (PBF) AM Technology



AM Technologies

AM process in which thermal energy (laser/EB) selectively fuses regions of a powder bed

o Selective Laser Sintering (SLS) o Selective Laser Melting (SLM) o Electron Beam Melting (EBM)

Directed Energy Deposition (DED) AM Technology: Blown Powder



AM Technologies

AM process in which focused thermal energy (laser/EB) is used to fuse powder materials by melting as they are being deposited o Laser Metal Deposition (LMD)/Laser

Engineered Net Shaping (LENS)

Directed Energy Deposition (DED) AM Technology: Wire-feed



AM Technologies

AM process in which focused thermal energy (laser/EB/arc welding) is used to fuse wire-feed materials by melting, being then deposited o Shaped Metal Deposition (SMD) o Laser Metal Deposition-wire (LMD-w)

Object



AM Applications

AM Applications: Sectors and Consumer Uses



AM Applications

Source: Wohlers Report 2014 Source: Wohlers Report 2014

Innovation o Freedom of design, complex shapes, inner cavities or foam/lattice structures,

impossible to produce by machining o Thin walls and shapes difficult to produce by casting o Light weight parts with lattice/foam inner structures



Key Benefits of AM

Process and cost optimization o Flexibility in design changes, agility of manufacturing operations, customized

design o Reduced time to market o No need for expensive and time-consuming part tooling and prototypes o Lower energy consumption and less waste

Enhanced mechanical properties

o Rapid cooling of each deposited layer o Finer grain size compared to casting or forming technologies

Geometrical accuracy



Challenges of AM

Distortions o Assembling problems o High contact pressures

Residual stresses

Porosity

Cracking, delamination, and swelling

Substrate adherence and warping

Scan strategy

Deposition strategy

Computational modeling and high-fidelity numerical simulation of Wire-feed (SMD), Blown Powder (LMD/LENS), and Powder Bed Fusion (SLS, SLM) AM processes



Goals

Validation of the computational model for the numerical simulation of Wire-feed (SMD), Blown Powder (LMD/LENS), and Powder Bed Fusion (SLS, SLM) AM processes

Sensitivity to different boundary conditions (heat convection, heat radiation, environment temperature, clamping temperature), material parameters (latent heat of fusion), and process parameters (power absorption, pre-heating, hatches-by-hatches activation sequence, layer-by-layer activation sequence) in the numerical simulation of Blown Powder (LMD/LENS) AM processes

Introduction Problem statement

o Coupled thermomechanical problem o Thermal problem o Mechanical problem

Computational model Numerical simulations Concluding remarks

Outline > Problem Statement

Problem Statement


Solution method: Isothermal operator split



Coupled Thermomecanical Problem

Thermal Problem Obtain the thermal variables (temperature) at constant configuration, keeping constant the mechanical variables (displacement/pressure)

Mechanical Problem Obtain the mechanical variables (displacement/pressure), keeping constant the thermal variables (temperature)

( )( ) ( ) [ ], div in 0,LH f Q Tθ θ θ= − + Ω×q

( )

( )[ ]

,in 0,1 0

pT

e pK

θ

θ

θ

∇ +∇⋅ + = Ω×

∇⋅ − − =

s u f 0

u

Energy balance equation: Strong form

Enthalpy rate: Heat capacity + latent heat terms

Solid/Liquid volume fractions

Heat flux: Fourier law



Thermal Problem

( )( ) ( ) [ ], div in 0,LH f Q Tθ θ θ= − + Ω×q

( )( ) ( )( ) ( )( ) ( )

( ) ( ) ( ) ( )

, ,, L L

L LL

L S

H f H fH f f

f

C L f C L f

θ θ θ θθ θ θ θ

θ

θ θ θ θ θ θ

∂ ∂= +

∂ ∂

= + = −

( ) ( )gradkθ θ θ= −q

( ) ( ) 1S Lf fθ θ+ =

Heat source: Wire-feed AM (SMD) Power input: Arc voltage: Current intensity: Power efficiency: MD volume: Wire-feed volume:

Heat source: Blown Powder (LMD/LENS)/Powder Bed Fusion AM (SLS, SLM) Laser power input: Power absorption: Melting pool volume:



Thermal Problem

eff effMD feed

P V IQV V

η η= =

effη

P

feedV

VI

absηP

poolVabs

pool

PQV

η=

MDV

Energy balance equation: Variational form

Boundary domain The boundary domain is changing during the AM process



Thermal Problem

( ) ( )( ) ( ) ( )( ) ( ) 0

, , ,

, ,clamp env

S

cond conv rad

C L f k Q

q q q

δθ θ θ θ δθ θ δθ

δθ δθ δθ∂ Ω ∂ Ω

− + ∇ ∇ =

− − + ∀ ∈

Normal heat flux by conduction (clamping system): Newton law

Considering contact between metallic materials, the value of the HTC by conduction is usually very high, around 1000 W/m2 K. Hence, the prescribed heat flux by conduction BC can be replaced by a prescribed temperature BC at the corresponding contact surface (clamping system).

Prescribed temperature (clamping system)



Thermal Problem

[ ]on 0,clamp clamp Tθ θ= ∂ Ω×

( )( ) [ ]on 0,cond cond clamp clampq h Tθ θ θ= − ∂ Ω×

Normal heat flux by convection (environment): Newton law

Normal heat flux by radiation (environment): Stefan-Boltzmann law Normal heat flux by convection+radiation (environment): Newton law



Thermal Problem

( )( ) [ ]on 0,conv conv env envq h Tθ θ θ= − ∂ Ω×

( ) [ ]4 4 on 0,rad rad rad env envq Tσ ε θ θ= − ∂ Ω×

( )( ) [ ]( ) ( ) ( )( )2 2

on 0,loss loss env env

loss conv rad rad env env

q h T

h h

θ θ θ

θ θ σ ε θ θ θ θ

= − ∂ Ω×

= + + +

( )( ) [ ]( ) ( )( )2 2

on 0,rad rad env env

rad rad rad env env

q h T

h

θ θ θ

θ σ ε θ θ θ θ

= − ∂ Ω×

= + +

Linear momentum balance equation: Mixed u/p strong form

Linear momentum balance equation: Mixed u/p variational form



Mechanical Problem

( )

( ) ( )[ ]

,in 0,1 0

pT

e pK

θ

θ

θθ

∇ +∇⋅ + = Ω×∇⋅ − − =

s u f 0

u

( ) ( )( ) ( ) ( )

( ) ( )( ) ( )

0, , , , ,

1, , , 0

sp

p e p p p pK

σ

θ

δ θ δ δ δ δ

δ θ δ δ δθ

∂ Ω∇ ⋅ + ∇ = + ∀ ∈

∇ ⋅ − − = ∀ ∈

u s u u f u t u u

u

Thermoelastic constitutive equations

Thermal volumetric strain: Thermal contraction + shrinkage due to phase-change



Mechanical Problem: Solid Phase

( ) ( )( )( ) ( ) ( )( ), 2 dev p

p K e

G

θθ θ

θ θ

= ∇ ⋅ −

= −

u

s u uε ε

( ) ( )cool pce e eθ θ θ= +

logpc L

S

eρρ

=

( ) ( )( ) ( )( )3 3coolref S S refe θ α θ θ θ α θ θ θ= − − −

Thermoelastic constitutive equations

Thermal volumetric strain: Thermal contraction + shrinkage due to phase-change




( ) ( )( )( ) ( ) ( )( ), 2 dev p

p K e

G

θθ θ

θ θ

= ∇ ⋅ −

= −

u

s u uε ε

( ) ( )cool pce e eθ θ θ= +

pcpc S L L S

S S L

V VVe

V Vρ ρρ

−∆= − = =

−

( ) ( )( ) ( )( )3 3coolref S S refe θ α θ θ θ α θ θ θ= − − −

J2-plasticity model: Yield function

Radius of the J2 yield surface

Associative plastic flow rule




( , , , ) : ( , )q R qθ θΦ = − −s q s q

( ) ( )( )0, 2 3R q qθ σ θ= −

( )

( )

( , , , )

( , , , ) 2 3

( , , , )

p

q

q

q

q

γ θ γ

ξ γ θ γ

γ θ γ

= ∂ Φ = − −

= ∂ Φ =

= ∂ Φ = − − −

σ

q

s q s q s q

s q

s q s q s q

ε

ξ

Elastic strain tensor: zero volumetric and deviatoric elastic strains Thermal volumetric strain: shrinkage due to phase-change

Mass density



Mechanical Problem: Mushy Phase

( ) ( )( ) ( )

log log1

1

pc

SS

L

L

Le e

f

θ θ θρ ρθ θρ

ρρ

= ==−

−

( ) ( ) ( )S S L Lf fρ θ θ ρ θ ρ= +

( )( )( )dev

0p

eθ θ

−

∇ ⋅ − =

=u

u

0ε ε

Elastic strain tensor: zero volumetric and deviatoric elastic strains Thermal volumetric strain: shrinkage due to phase-change

Mass density




( ) ( ) ( )S S L Lf fρ θ θ ρ θ ρ= +

( ) ( ) ( )( )

( )( )

( ) ( )pc

pc L SS

L

L L

L

V V Ve e f

V Vθ θ θ ρ ρ θ ρ ρθ θ θ

θ θ ρ ρ∆

= = − = = =− − −

( )( )( )dev

0p

eθ θ

−

∇ ⋅ − =

=u

u

0ε ε

Rate-dependent J2-plasticity model: Yield function Associative plastic flow rule

Non-Newtonian model




( )( , , , ) : mq θθ ηγΦ = =s q s

( , , , )

( , , , ) 0

( , , , )

p

q

q

q

q

γ θ γ

ξ γ θ

γ θ

= ∂ Φ = = ∂ Φ =

= ∂ Φ =

σ

q

s q s s

s q

s q 0

ε

ξ

( ) ( ) 132

2 dev3

mp p

p

θσ η εε

−= =s

ε ε

Elastic strain tensor: zero volumetric and deviatoric elastic strains Thermal volumetric strain: zero thermal strain Strain tensor: purely plastic deviatoric



Mechanical Problem: Liquid-like Phase

( )( )( )dev

0p

eθ θ

−

∇ ⋅ − =

=u

u

0ε ε

( ) 0 0eθ θ = ⇒ ∇⋅ =u

( ) ( )( ) ( )

dev dev

dev

tr 0, p p

p

= ∇⋅ = = =

= =

u u u

u u

ε ε ε ε

ε ε ε

Rate-dependent J2-plasticity model: Yield function Associative plastic flow rule

Norton-Hoff model



Mechanical Problem: Liquid-like Phase

( , , , ) :q θ ηγΦ = =s q s

( , , , )

( , , , ) 0

( , , , )

p

q

q

q

q

γ θ γ

ξ γ θ

γ θ

= ∂ Φ = = ∂ Φ =

= ∂ Φ =

σ

q

s q s s

s q

s q 0

ε

ξ

23

p pp

σ η ηε

= = =s

ε ε ε

Introduction Problem statement Computational model

o FE Activation strategy o Welding pool volume o Scanning path: Common Layer Interface (CLI) o Incompressibility constraint

Numerical simulations Concluding remarks

Outline > Computational Model

Computational Model


FE Activation strategy: Passive/active elements • A FE mesh of the full domain is considered • Elements are classified as: passive and active

o Passive elements are defined by setting their material properties to very low fictitious values. They are computed and assembled

o Active elements are defined by setting their material properties to their physical values. They are computed and assembled

o Passive elements are switched on to active elements on each time step, according to the welding/melting path



FE Activation Strategy

Drawbacks • High computational cost, as all elements have to be computed and assembled,

and the full system of equations has to be solved • Too lower values for the fictitious material properties may lead to ill-

conditioning of the system of equations • Strains and temperature gradients of passive elements are accumulated,

transforming into spurious stresses and heat fluxes when they are activated

FE Activation strategy: Born/dead elements • A FE mesh of the full domain is considered • Elements are classified as: active, inactive, and activated

o Active elements are computed and assembled o Inactive elements are not computed o Activated elements are those elements that are switched on at each time step,

according to the welding/melting path. They are computed and assembled




Drawbacks • Profile of solution matrix and number of equations is changing • Activated elements share nodes with active elements and material is not free

to expand during the heating phase, leading to non-zero initial displacements and spurious thermal stresses when they are activated. They have to be removed as part of the activation procedure.

Control volume and octree-based searching algorithm • To set up the activation process, a control volume is defined by the cross

section of the layer and the welding speed. • All the elements belonging to this domain are identified using an octree-based

searching algorithm and their status is changed from inactive to activated elements.




M. Chiumenti, M. Cervera, A. Salmi, C. Agelet de Saracibar, N. Dialami, K. Matsui (2010), Finite element modeling of multi-pass welding and shaped metal deposition processes, Computer Methods in Applied Mechanics and Engineering 199 (37-40) 2343-2359

Control volume: wire-feed (SMD) AM processes

The MD volume must be equal to the volume of wire-feed melted by the arc welding:

The cross section area of the MD is given by:




MD feedV V=

2

4

MD MD MD

feed wire feed

V S v t

V v tπ φ

= ∆

= ∆

2

4feed

MD wireMD

vS

vπ φ=

M. Chiumenti, M. Cervera, A. Salmi, C. Agelet de Saracibar, N. Dialami, K. Matsui (2010), Finite element modeling of multi-pass welding and shaped metal deposition processes, Computer Methods in Applied Mechanics and Engineering 199 (37-40) 2343-2359

Control volume: Blown Powder (LMD/LENS)/Powder Bed Fusion (SLS, SLM) AM processes

The cross section area of the MD is given by:

where,




MD MD MDS h b=

MD MD MDV S v t= ∆

average thickness of MD layer

average width of MD layer

MD

MD

hb

==

Pool control volume: Blown Powder (LMD/LENS)/Powder Bed Fusion (SLS, SLM) AM processes The cross section area of the pool control volume is given by:

where is the laser spot size and is the welding pool penetration in the substrate.

Octree-based searching algorithm is used to look for elements belonging to the pool control volume

Welding pool volume for Blown Powder (LMD/LENS)/Powder Bed Fusion (SLS, SLM) AM processes, where the heat source/power density is applied



Welding Pool Volume

pool pool poolS r h=

pool pool MDV S v t= ∆

poolh

1e

pool

n epool poole e V

V V= ∈

=∑

poolr

The Common Layer Interface (CLI) is a universal format for the input of geometry data to model fabrication systems based on layer manufacturing technologies (LMT), such as LMD/LENS, SLS, or SLM

Definition of the scanning sequence using the CLI format o Layer defines the layer entry level o Polyline defines the section boundaries of the layer o Hatches define the filling section of the layer



Scanning Path: Common Layer Interface (CLI)

LMD/LENS scanning sequence: Polyline and hatches for a layer



Scanning Path: Common Layer Interface (CLI)

Isochoric behaviour: Liquid-like phase • Purely viscous Norton-Hoff behaviour • Zero thermal strains

Isochoric plastic behaviour: Solid phase • J2 Thermo-elasto-plastic material model • Isochoric plasticity

FE technology for incompressibility constraints M. Cervera, M. Chiumenti, Q. Valverde, C. Agelet de Saracibar (2003), Mixed linear/linear simplicial elements for

incompressible elasticity and plasticity, Computer Methods in Applied Mechanics and Engineering 192 (49), 5249-5263



Incompressibility Constraint

• Hexahedral elements o Q1P0 elements o B-bar elements o EAS elements

• Tetrahedral elements o ASGS stabilized P1P1 elements o OSS stabilized P1P1 elements

Incompressibility

Quasi-Incompressibility

Introduction Problem statement Computational model Numerical simulations

o SMD Boss o SMD 10 Layers Strip Band o SMD Hanging Lug of a Jet Engine Turbine Blade o LMD/LENS 10 Layers Strip Band

Concluding remarks

Outline > Numerical Simulations

Numerical Simulations


Experimental setting at ITP: SMD of a Boss



SMD – Boss

TC1

TC2

Upper surface Lower surface

TC3

TC4

Clamping system

Experimental SMD Experimental SMD Experimental SMD

Before AM After AM After AM

SMD welding path, gometry, and thermocouple locations



SMD – Boss

114mm

45mm

50mm

10mm

20mm

Th2

Th4

Th1Th3 114mm

45mm

50mm

10mm

20mm

Th2

Th4

Th1Th3

Th3 Th4

Th2Th1

45mm

15mm

Th3 Th4

Th2Th1

45mm

15mm

50mm

SMD Process parameters



SMD – Boss

Gas Tungsten Arc Welding (GTAW)

Voltage 12 [V]

Current Intensity 180 [A]

Power Efficiency 0.75 (SMD) 0.90 (pre-heating)

SMD

Welding speed 22 [mm/min] (SMD) 20 [mm/min] (pre-heating)

Layer height 1.471 [mm]

Pre-heating passes 6

Layers 34

Material properties: INCONEL 718 (wire feed + base material)



SMD – Boss

INCONEL 718

Solidus Temperature 1260 [ºC]

Liquidus Temperature 1335 [ºC]

Latent Heat 240 [kJ/kg]

Thermal Shrinkage 3 %

Mass Density Temperature dependent

Specific Heat Temperature dependent

Thermal Conductivity Temperature dependent

Yield Stress Temperature dependent




SMD – Boss

Mass density

Conductivity

Specific heat

Yield stress

Thermal and Mechanical BC



SMD – Boss

Thermal BC: Convection and radiation

HTC convection 25 [W/m2 K]

Emissivity 0.6

Environment temperature 20 [ºC]

Mechanical BC

Isostatic clamping of the base plate

FE mesh



SMD – Boss

FE mesh: 6,114 Q1P0 hexahedral elements

Temperature map evolution and temperature contour fill at the end of the SMD process, just before the cooling phase



SMD – Boss

Temperature contour fill at the end of the SMD process, just before the cooling phase

Temperature evolution at the 4 thermocouple locations



SMD – Boss

Experimental setting at ITP: SMD of a 10-layers strip band



SMD – 10 Layers Strip Band

Before AM After AM

Clamping system and TC locations on upper surface

Geometry, clamping system and thermocouple locations









Voltage 12 [V]


Power Efficiency 0.75 (SMD) 0.90 (pre-heating)

SMD

Welding speed Variable

Layer height Variable


Layers 10





Pre-heating (1-2) and SMD (3-12) paths Path Welding Speed

[mm/min] Area [mm2]

Width [mm]

Height [mm]

Area [mm2]

1 100 12.000 1.000

2 100 12.000 1.000

3 200 13.570 10.400 1.212 13.570

4 200 13.570 8.500 1.436 13.570

5 300 9.050 7.860 1.106 9.050

6 350 7.760 7.260 1.026 7.760

7 375 7.240 6.660 1.040 7.240

8 400 6.790 6.040 1.069 6.790

9 425 6.390 5.390 1.118 6.390

10 450 6.030 4.690 1.197 6.030

11 475 5.710 3.920 1.327 5.710

12 500 5.430 3.010 1.567 5.430

SMD: 10 layers

Pre-heating: 2 passes





INCONEL 718













Mass density

Conductivity

Specific heat

Yield stress







Emissivity 0.6


Mechanical BC

Isostatic clamping of the base plate

FE mesh




FE mesh: 54,862 Q1P0 hexahedral elements

Vertical displacement contour fill after the cooling phase and temperature map evolution




Vertical displacement contour fill after the cooling phase

Temperature map evolution

Temperature evolution at the 6 thermocouple locations




0

100

200

300

400

500

600

700

800

900

0 50 100 150 200 250 300 350 400 450 500

Time (sec)

Tem

p.

Point1 Exp.

Point1 Num.

Point4 Exp.

Point4 Num.

0

100

200

300

400

500

600

700

800

900

0 50 100 150 200 250 300 350 400 450 500

Time (sec)

Tem

p

Point3 Exp.

Point3 Num.

Point5 Exp.

Point5 Num.

0

100

200

300

400

500

600

700

0 50 100 150 200 250 300 350 400 450 500

Time (sec)

Tem

p.

Point2 Exp.

Point2 Num.

Point6 Exp.

Point6 Num.

Lower surface: TC1 and TC4

Upper surface: TC2 and TC6 Upper surface: TC3 and TC5

Deformation of transversal and longitudinal sections




Transversal sections Longitudinal sections

Residual stresses after cooling: J2 stress




Experimental setting at ITP: SMD of a hanging lug of a jet engine turbine blade



SMD – Hanging Lug of a Jet Engine Turbine Blade

Before AM After AM After AM

After AM






Voltage 12.5 [V]


Efficiency 0.75 (SMD) 0.90 (pre-heating)

SMD

Welding speed 110 [mm/min]

Deposition height 0.7 [mm]


Layers 10





INCONEL 718













Mass density

Conductivity

Specific heat

Yield stress







Emissivity 0.6


Mechanical BC

Isostatic clamping of the flange

Fixed

Uz = 0

Subcomponent geometry and FE mesh detail




800,455 OSS P1P1 tetrahedral elements (full component) 350,000 OSS P1P1 tetrahedral elements (blue+green domains) for SMD

Temperature map evolution and temperature contour fill at different time steps




Temperature map evolution Temperature contour fill at different time steps

Residual stresses contour fill: J2 stress and maximum principal stress




J2 stress Maximum principal stress

Blown Powder technology at SKLSP, Xi’an, China LSF-III B Laser Solid Forming (LSF) machine at SKLSP, Xi’an, China



LMD/LENS – 10 Layers Strip Band

Experimental setting at SKLSP: LMD/LENS of a 10 layers strip band




Substrate and supporting system After the AM process

Before AM After AM

Geometry, clamping area and location of thermocouples




Lower surface

Upper surface

Process parameters for the LSF machine




LMD/LENS process parameters: LSF machine

Laser power 2 [kW]

Laser beam spot size 1.6 [mm]

Offset distance 0.8 [mm]

Up-lift height 0.3 [mm]

Scanning speed 10 [mm/s]

Back speed 50 [mm/s]

Powder feeding 8.5 [g/min]

Layers 10

Hatches for each layer 7

Process parameters used in the LMD/LENS numerical simulation




LMD/LENS process parameters: Numerical simulation

Laser power 2 [kW]

Power absorption 16.5 %

Scanning speed 10 [mm/s]

Back speed 50 [mm/s]

Penetration 0.28 [mm]

Layer thickness 0.28 [mm]

Layer width 1.75 [mm]

Overlapping 50 %

Layers 10

Hatches for each layer 7

Material properties: Ti-6Al-4V (powder + substrate material)




Ti-6Al-4V







Thermal Expansion Temperature dependent

Elastic Young Modulus Temperature dependent


Material properties: Ti-6Al-4V (powder + substrate material)




Mass Density Conductivity Specific heat

Yield stress LTE coefficient Young modulus

Thermal BC: Heat convection and radiation (reference model 1)

TD HTC loss by convection+radiation (model 2)




Thermal BC: Heat convection and radiation (environment)


Emissivity 0.7

Environment temperature Time dependent

Temperature [ºC] HTC loss [W/m2 K]

20 6

200 10

400 20

600 40

800 70

1000 110

1200 160

Linear TD HTC loss by convection+radiation (simplified model 3)

Linear TD HTC loss by convection+radiation (simplified model 4)

Thermal BC: Heat conduction





20 6

1200 160


20 6

1200 90

Thermal BC: Heat conduction (clamping system)

HTC conduction 50 [W/m2 K]

Clamping temperature Time dependent

Time-dependent environment and clamping temperature




Environment temperature: measured and numerical data used in the simulation

Clamping temperature: measured and numerical data used in the simulation

TC were placed too close to the process area, being affected by the heat radiation during the AM process

FE mesh




FE mesh detail

AM Strip band: 80 mm x 7 mm x 2.8 mm

FE mesh: 10,050 Q1P0 hexahedral elements Time step: automatic with Cu = 1

Temperature map evolution o Pre-heating phase o AM phase: 10 layers, 7 hatches/layer o Cooling phase




Substrate oxidation and temperature contour fill at the end of the AM process




Temperature evolution at TC CH1-CH3: lower surface




Zoom box Numerical

Experimental

Experimental Numerical

Very good agreement between numerical and experimental temperature evolution at TC

Temperature evolution at TC CH4-CH5: upper surface




Zoom box Numerical

Experimental

Experimental

Numerical

Very good agreement between numerical and experimental temperature evolution at TC

TC on upper surface were shielded to protect them from radiation, but still

suffer from heat radiation effects

LMD/LENS vs SLS AM Technology: temperature at TC CH1 and CH2




SLS

LMD/LENS

LMD/LENS

SLS

LMD/LENS and SLS are quite different AM technologies, but the numerical

results look very similar

LMD/LENS SLS

Sensitivity to power absorption: temperature at TC CH1




Power absorption during AM process set to 16.5%

Power absorption set to 30%



Power absorption set to 16.5%


Power absorption set to 16.5%

Zoom box

High sensitivity to power absorption

Sensitivity to pre-heating phase: temperature at TC CH2




Power absorption during AM phase set to 16.5%, while during pre-

heating phase is set to 10%

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Cooling

Without pre-heating

With pre-heating

AM

Pre-

heat

ing

Without pre-heating

With pre-heating

Pre-heating shows a lower power absorption

Sensitivity to latent heat: temperature at TC CH1-CH3




Latent heat contribution is negligible

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Sensitivity to heat radiation/convection: temperature at TC CH1-CH3





Only convection

Convection + radiation

Only convection


Only convection with reduced power absorption

Heat radiation is the key heat dissipation mechanism

Sensitivity to heat radiation/convection: temperature at TC CH2




Only radiation


TD HTC loss (convection+radiation)

Linear convection with max HTC = 160

Linear convection with max HTC = 90

Heat loss due to radiation+convection can be simulated using a Newton law

with a TD HTC


Sensitivity to environment temperature: temperature at TC CH1-CH3




Environment temperature set to 25 ºC

Measured environment temperature at TC

Environment temperature used in the reference simulation

Sensitivity to environment temperature is negligible

Environment temperature: measured and simplified used in

the reference numerical simulation

Numerical reference: Time-dependent

environment temperature

Sensitivity to HTC conduction: temperature at TC CH1




HTC conduction = 1000

Most suitable HTC was 50, far from the typical value of 1000 when contact is almost perfect (bolts torque was very

low because applied just manually)

Clamping temperature: measured and simplified used in the

reference numerical simulation

Numerical reference: HTC conduction = 50

Measured clamping temperature at TC

Clamping temperature used in the reference simulation

Sensitivity to clamping temperature: temperature at TC CH1




Clamping temperature set to 25 ºC

Measured clamping temperature at TC

Clamping temperature used in the reference simulation

Sensitivity to clamping temperature is negligible during AM phase, but

better accuracy in cooling phase if clamping temperature is known

Clamping temperature: measured and simplified used in the

reference numerical simulation

Numerical reference: Time-dependent clamping

temperature

Sensitivity to AM activation sequence: temperature at TC CH1-CH3




By hatches

By layers

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Sensitivity to AM activation sequence: temperature map evolutions




By hatches By layers

Computational cost




Strategy CPU time [s] Computational cost ratio [%]

Reference 1356 100

No phase-change 473 35

Hatch-by-hatch 61 4

Layer-by-layer 29 2

Introduction Problem statement Computational model Numerical simulations Concluding remarks

Outline > Concluding Remarks

Concluding Remarks


Remarkable accuracy in the numerical simulation of DED AM technologies (wire-feed SMD and blown powder LMD/LENS), has been achieved



Concluding Remarks

High sensitivity to power absorption. Power absorption for blown powder (LMD/LENS) technology, is very low (around 15% of the power input). Pre-heating phase shows a lower power absorption (around 10% of the power input)

Latent heat of fusion contribution due to liquid-solid phase-change can be neglected, saving up to a 65% of the total CPU time

Heat radiation is the key mechanism to dissipate heat through the boundaries with the environment. It cannot be neglected, but Stefan-Boltzmann law can be replaced by a Newton law using a TD HTC. Total heat loss by convection + radiation can be conveniently simulated using a single Newton law with a TD HTC

Most suitable HTC by conduction at the clamping system was 50 W/m2 K, being far from the typical value of 1000 W/m2 K when contact is almost perfect. This can be explained by the fact that the bolts torque was very low because applied just manually



Concluding Remarks

Time variation of the environment (closed chamber) temperature and clamping system temperature can be neglected. Nevertheless, better accuracy can be achieved in the cooling phase if the time-dependent clamping temperature is available

CPU time can be drastically reduced using a hatch-by-hatch or even a layer-by-layer AM activation sequence. Average temperature evolution can be well captured, but the accuracy on the local thermal history is lost, and the mechanical response can be compromised

Efficient Manufacturing for Aerospace Components Using Additive Manufacturing, Net Shape HIP and Investment Casting (EMUSIC), H2020-MG-2015_SingleStage-A, Project no. 690725, EC

Computer Aided Technologies for Additive Manufacturing (CAxMan), H2020-FoF-2015_CNECT, Project no. 680448, EC

Computational Cloud Services and Workflows for Agile Engineering. HPC Workflow for Simulation of Additive Manufacturing for Improving the Production of Gearboxes (CLOUDFLOW), CloudFlow-2, Project no. 609100, EC

Virtual Engineering for Robust Manufacturing with Design Integration (VERDI), FP6-2003-AERO-1, AST4-CT-2005-516046, Project no. 516046, EC

State Administration of Foreign Experts Affairs of China through the High-end Experts Recruitment Program

National Natural Science Foundation of China, Grants 51323008 and 51271213 National Basic Research Program of China, Grant 2011CB610402 National High Technology Research and Development Program of China, Grant

2013AA031103 Specialized Research Fund for the Doctoral Program of Higher Education of China, Grant

20116102110016 Yasmine Lebbar


Outline > Acknowledgments

Acknowledgments


On the Numerical Simulation of AM Processes M. Chiumenti1,2, M. Cervera1, N. Dialami1, C. Agelet de Saracibar1 1 International Center for Numerical Methods in Engineering (CIMNE) UPC BarcelonaTech, Barcelona, Spain W. Huang2, X. Lin2, L. Wei2, Y. Zheng2, L. Ma2 2 State Key Laboratory of Solidification Processing (SKLSP) Northwestern Polytechnical University (NWPU), Xi’an, China

2nd International Conference on Computational Methods in Manufacturing Processes Liège, Belgium, 18-20 May 2016

ICOMP 2016