Numerical Simulation of the Temperature Distribution and ... · Numerical Simulation of the...
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L. Wang, et al. @ SFF Symposium, 2006
Numerical Simulation of the Temperature Distribution and Microstructure
Evolution in the LENS™ Process
1. Center for Advanced Vehicular Systems, Mississippi State University2. Mechanical Engineering, Mississippi State University3. ESI Group, Bloomfield Hills, MI
L. Wang1, S. Felicelli2, Y. Gooroochurn3, P.T. Wang1, M.F. Horstemeyer1
The Seventeenth Solid Freeform Fabrication Symposium The Seventeenth Solid Freeform Fabrication Symposium Aug 14 Aug 14 -- 16, 2006, Austin, Texas16, 2006, Austin, Texas
L. Wang, et al. @ SFF Symposium, 2006
Outline
Introduction
Objectives
Finite Element Modeling
Results and Discussions
Conclusions
Future Work
L. Wang, et al. @ SFF Symposium, 2006
IntroductionLaser beam and powder
delivery nozzleMirror or other beamguiding means
Laser
Carrier gasLens
Shroudgas inlet
X-Y positioningstages
Materialdepositionhead
Temperature distribution in molten pool (Hofmeister et al. 1999)
Powder materialsupply
Z-axis positioning offocusing lens and powder delivery nozzle assembly
Laser Engineered Net Shaping (LENSTM) Schematic
L. Wang, et al. @ SFF Symposium, 2006
IntroductionA variety of materials can be used:
Stainless Steel (SS410, SS316)Ti-based alloy (Ti-6Al-4V)Inconel, copper, aluminum, etc.
Application:Aerospace repair & overhaulRapid prototyping and 3D structure fabricationProduct development for aerospace, defense, and medical markets, etc.
Advantages:Low cost & time saving Enhanced design flexibility and automationHighly localized heat-affected zone (HAZ)Superior material properties (strength and ductility)
Processing Blade
Processing Bar
L. Wang, et al. @ SFF Symposium, 2006
Introduction
The mechanical properties are dependent on the microstructure of the material, which in turn is a function of the thermal history of solidification.
An understanding of the thermal behavior of the fabricated part during the LENS process is of special interest.
Numerical simulation methods have the potential to provide detail information of the thermal behavior.
L. Wang, et al. @ SFF Symposium, 2006
Objectives
Develop a 3-D model to simulate 10-pass single build plate LENS deposition of 410 stainless steel (SS410) powder with SYSWELD finite element code. Predict the temperature distribution and cooling rate surrounding the molten pool and compared with experimental data available in the literature. Optimize the process parameter (laser power) in order to achieve a pre-defined molten pool size for each pass. Investigate the effect of the thermal cycles on the phase transformation and consequent hardness.
L. Wang, et al. @ SFF Symposium, 2006
20
(Unit: mm)
5
10V
Substrate
Weld direction: Same direction for each pass.Material properties of the deposited part and the substrate are the same.
Process parameters ValuesWidth of the part 1.0 mm
Thickness for each layer 0.5 mm
Laser beam travel velocity 7.62 mm/s
Moving time of the laser beam for each pass
1.3 s
Idle time of consecutive layers deposition
0.7 s
Time to finish one layer 2 s
Total time to finish the part 20 s
10 pass single build part
Geometry & Process Parameters
L. Wang, et al. @ SFF Symposium, 2006
Thermal Properties (SS410)
Thermal properties depend on the temperature, and the phase proportions.
L. Wang, et al. @ SFF Symposium, 2006
Mesh Structure
A dense mesh was used for the plate and the contact area with the substrate, where higher thermal gradients are expected.
• Number of nodes: 104,535• Number of elements: 132,400• Element size in the part: 0.1 X 0.1 X 0.1 mm3
L. Wang, et al. @ SFF Symposium, 2006
Mathematical Model
Modified heat conduction equation:
P ρ- phase proportionT C
t λji, Q
)(TLij
ijAi j→
- temperature
- phase indexes
- latent heat of transformation
- mass density- specific heat
- Proportion of phase transformed to in time uniti
- time - thermal conductivity
- heat source
j
Thermal properties depend on the temperature, and the phase proportions.The latent heat effects due to phase changes are modeled with the specific heat variation.
L. Wang, et al. @ SFF Symposium, 2006
Dummy material method is applied to the element activation:
M1: Deposited layers + substrateMaterial with actual thermal properties and phase transformation
M2: Layer being deposited Material with actual thermal properties and starting with dummy phaseDummy phase → Austenite phase (T>Taus)
M3: Layers to be deposited Material with dummy low thermal properties and without phase transformation
Element Activation Technique
Fixed mesh is used for the plate and substrate.
M1
M2
M3
V
L. Wang, et al. @ SFF Symposium, 2006
Heat Source
hzhrrrr ie
eo))(( −−
−=
222 )()( tvyyxxr oo ⋅−−+−=
⎟⎟
⎠
⎞
⎜⎜
⎝
⎛⎟⎟⎠
⎞⎜⎜⎝
⎛−⎟
⎠⎞
⎜⎝⎛ −=
2
020
1exp12rr
hz
hrPQr π
3D Conical Gaussian Function
rQ P- Input energy density (W/mm3) - Absorbed laser power (W)
Part of energy generated by the laser beam is lost before being absorbed by the part.Absorbed laser power is used in the calculation. The nominal laser power is calibrated by matching the predicted temperature profile with measured data.
rQ
L. Wang, et al. @ SFF Symposium, 2006
Initial and Boundary ConditionsInitial condition
Boundary condition on the bottom of the substrate
Boundary conditions for all other surface
As new layers are activated, the surfaces are increased and the boundary conditions are updated.
0)0,,,( TtzyxT ==
0)0,,( TzyxT == 0>tfor
( ) ( ) Laserrea QTTTThnTk ΩΩΩΩ −−+−=⋅∇ 44)( εσr
L. Wang, et al. @ SFF Symposium, 2006
Model Calibration
800
1000
1200
1400
1600
1800
0 1 2 3 4
-4000
-3000
-2000
-1000
00 1 2 3 4
Distance (mm)
Tem
pera
ture
(°C
)C
oolin
g R
ate
(°C
/s)
4 mm
ModelingMeasured (Hofmeister et al., 1999)
Temperature distribution (2-D View)
The calibration calculation is performed only for the deposition of the top layer (the 10th layer).T0 = 600°C, Pabs = 100W, Pl = 275W, E = 36.4% (30-50%) (Unocic and DuPont, 2004)
L. Wang, et al. @ SFF Symposium, 2006
Molten Pool Size2.0 mm
10
8
6
4
2300
350
400
450
500
550
600
1 2 3 4 5 6 7 8 9 10Pass Number
Nom
inal
Las
er P
ower
(W)
The molten pool size is determined by melting temperature (1450°C for SS410)One and a half layers are melted for each passAbout 5% decrease in laser power is needed from one layer to the next subsequent layer in order to keep a fairly constant pool size
L. Wang, et al. @ SFF Symposium, 2006
Temperature Distribution
Laser beam is at the center of the 5th pass Laser beam is at the center of the 10th pass
3D temperature distribution for 10-pass LENS processSimilar molten pool size and temperature distribution surrounding the molten pool size are obtained by both cases.
L. Wang, et al. @ SFF Symposium, 2006
Thermal Cycles
Thermal cycles for the mid-points of layers 1, 3, 5, and 10 of the built plate.
Cross-section micrograph of H13 tool steel thin wall*
Hardness versus distance from top of wall** Griffith et al., Thermal Behavior in the LENS process,” J. Mater. Des. 20 (1999) 107-114.
1st layer3rd layer 5th layer 10th layer
Ms
(s)
(°C)
L. Wang, et al. @ SFF Symposium, 2006
Cooling Rates
Cooling rates for the mid-points of layers 1, 3, 5, and 10 of the built plate.
(s)
(°C/s)
Max. cooling rate for 1st layer
Max. cooling rate for each layer
L. Wang, et al. @ SFF Symposium, 2006
Temperature Contour Movie
L. Wang, et al. @ SFF Symposium, 2006
ConclusionsA 3-D model has been developed to predict the thermal cycles and cooling rates during the 10-pass LENS process of a SS410 plate with SYSWELD. The model predicts temperature profiles and cooling rates that agree qualitatively and quantitatively well with measured data.About 5% decrease in laser power for each pass is required in order to keep the molten pool size in the pre-defined range. The tempered martensite is transformed at the lower layers due to the thermal cycles, which will cause the hardness of the upper part to be higher than that of the lower part.
L. Wang, et al. @ SFF Symposium, 2006
Future Work
Experiments will be performed to measure the thermal profiles and temperature gradients for SS410 plate to calibrate the current model.
Using the calculated thermal profiles, the phase proportions and hardness of the LENS material will be predicted with SYSWELD.
Measurements in hardness and microstructures will be performed to calibrate the model.
L. Wang, et al. @ SFF Symposium, 2006
Acknowledgements
Dr. John Berry (ME, Mississippi State University)
Jim Bullen (Optomec Co.)
Benton Gady (National Automative Center)
The project is sponsored by U.S. Army TACOM.