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Research Papers

A Generalized Feed-Forward Dynamic Adaptive Mesh Refinement and Derefinement Finite-Element Framework for Metal Laser Sintering—Part II: Nonlinear Thermal Simulations and Validations2

[+] Author and Article Information
Deepankar Pal

Assistant Professor
Department of Mechanical Engineering,
J. B. Speed School of Engineering,
University of Louisville,
Louisville, KY 40292
e-mail: d0pal001@louisville.edu

Nachiket Patil

3DSIM, LLC,
1794 Olympic Parkway, Suite 110,
Park City, UT 84098
e-mail: nachiket.patil@3dsim.com

Khalid Haludeen Kutty

Underwriters Laboratories,
20 Kian Teck Lane #02-00PT,
Singapore 627854, Singapore
e-mail: khalidrafi@gmail.com

Kai Zeng

3DSIM, LLC,
1794 Olympic Parkway, Suite 110,
Park City, UT 84098
e-mail: kai.zeng@3dsim.com

Alleyce Moreland

Research Division,
Mound Laser and Photonics Center,
Kettering, OH 45420
e-mail: AlleyceMoreland@mlpc.com

Adam Hicks

Research Division,
Mound Laser and Photonics Center,
Kettering, OH 45420
e-mail: AdamHicks@mlpc.com

David Beeler

Research Division,
Mound Laser and Photonics Center,
Kettering, OH 45420
e-mail: DavidBeeler@mlpc.com

Brent Stucker

3DSIM, LLC,
1794 Olympic Parkway, Suite 110,
Park City, UT 84098
e-mail: brent.stucker@3dsim.com

1Corresponding author.

Manuscript received January 29, 2014; final manuscript received November 9, 2015; published online January 6, 2016. Assoc. Editor: Jack Zhou.

2Part I of this paper published in J of Manuf. Sci. Eng 137(4), 041001.

J. Manuf. Sci. Eng 138(6), 061003 (Jan 06, 2016) (10 pages) Paper No: MANU-14-1041; doi: 10.1115/1.4032078 History: Received January 29, 2014; Revised November 09, 2015

A novel multiscale thermal analysis numerical tool has been developed to address the micro–macro interactions involved in localized melting and sintering processes, such as laser sintering of metals exhibiting nonlinear thermal response. The method involves extension of a feed-forward dynamic adaptive mesh refinement and derefinement finite-element framework to incorporate nonlinear thermal phenomenon in the vicinity of the energy source and further reduce computational time and complexity when simulating spatiotemporally periodic problems posed by metal laser sintering. The thermal and microstructural predictions computed using this framework are in good agreement with the thermal contours measured using a forward-looking infrared (FLIR) imaging system and microstructures observed using an optical microscope.

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References

Figures

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Fig. 3

(a) Dynamically enriched mesh at an arbitrary initiation co-ordinate (3,3) with its blownup fine mesh in (b). The element and node numbers can be automatically assigned to the mesh in the developed mesh generation tool. Only the x–y plane is shown for clarity. The mesh is three-dimensional in nature.

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Fig. 4

((a)–(f)) The movement of the dynamic mesh with the laser heat source in the x–y plane of the metal laser sintering machine. The arrow shows the direction of heat source motion.

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Fig. 5

Temperature-dependent conductivity (k) and volumetric heat capacity (ρc) in W/(m K) and J/(m3 K), respectively, plotted against temperature in Kelvin

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Fig. 2

Surface boundary conditions for the DMLS problem, which include convection, laser flux, and fixed temperature boundary conditions

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Fig. 1

(a) Geometry and (b) boundary conditions for metal laser sintering of Ti6Al4V material comprised of a base plate, powder layer, and Gaussian laser beam. Forced argon in the chamber causes convection which is also considered as a boundary condition for the problem, though not shown here.

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Fig. 6

Comparison of thermal contours (in Kelvin plotted against x–y in meters) between linear ((a) and (b)) and nonlinear ((c) and (d)) scenarios. The melt pool diameter in (b) is 125 μm, whereas in (d) it is 107 μm. The spatial gradient due to laser spot decays steeply in the y direction for the nonlinear scenario (∼230 μm against 353 μm observed for the linear case).

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Fig. 7

Thermal contours (in Kelvin) at various locations (in meters) during laser scanning of the first layer

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Fig. 8

Plots showing the nonlinear thermal distribution (a), martensitic α′ region evolution (b), martensitic α′ area phase fraction with respect to the total area of the inset (c), solidified region evolution (d), and solidified area phase fraction with respect to the total area of the inset (e). It can be observed that the solidified area fraction (34.8%) is slightly smaller than that of the martensitic α′ area fraction (35.2%).

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Fig. 9

(a) Internal defect design, (b) unmelted particles at an internal surface pertaining to the central cylindrical defect of the build, and (c) unmelted particle removal using sand blasting operation at the external surface of the build [35]

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Fig. 10

Computed thermal history at the center-top of the base plate as additional layers are built over it

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Fig. 11

Layer-by-layer thermal evolution (in Kelvin) in metal laser sintering as a function of location (in meters): (a) first layer, (b) second layer, and (c) third layer

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Fig. 12

Simulated thermal contours (473 K and 403 K) are present at the same location as shown via experiments (Fig. 13) while using the same process parameters. Temperature color-bar is shown in Kelvin.

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Fig. 13

Experimentally obtained thermal contours. The FLIR camera has been calibrated using a hot plate.

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Fig. 14

Transverse prior β grain diameter (∼100 μm) matches the predicted melt pool diameter (∼100 μm)

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Fig. 15

Melt pool validation showing the melt pool asymmetry due to the existence of a solid (conductor on one side) and powder (insulator on the other side) surrounding the melt pool (computed on layer 8)

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