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Research Papers

On the Simulation Scalability of Predicting Residual Stress and Distortion in Selective Laser Melting

[+] Author and Article Information
C. Li, Z. Y. Liu, Y. B. Guo

Department of Mechanical Engineering,
The University of Alabama,
Tuscaloosa, AL 35487

X. Y. Fang

Institute for Advanced Manufacturing,
Shandong University of Technology,
Zibo 255049, China

1Corresponding authors.

Manuscript received July 21, 2017; final manuscript received December 12, 2017; published online February 13, 2018. Assoc. Editor: Hongqiang Chen.

J. Manuf. Sci. Eng 140(4), 041013 (Feb 13, 2018) (10 pages) Paper No: MANU-17-1467; doi: 10.1115/1.4038893 History: Received July 21, 2017; Revised December 12, 2017

Rapid heating and cooling thermal cycle of metals in selective laser melting (SLM) generates high tensile residual stress which leads to part distortion. However, how to fast and accurately predict residual stress and the resulted part distortion remains a critical issue. It is not practical to simulate every single laser scan to build up a functional part due to the exceedingly high computational cost. Therefore, scaling up the material deposition rate via increasing heat source dimension and layer thickness would dramatically reduce the computational cost. In this study, a multiscale scalable modeling approach has been developed to enable fast prediction of part distortion and residual stress. Case studies on residual stress and distortion of the L-shaped bar and the bridge structure were presented via the deposition scalability and validation with the experimental data. High residual stress gradient in the building direction was found from high tensile on the surface to high compressive in the core. The part distortion can be predicted with reasonable accuracy when the block thickness is scaled up by 50 times the layer thickness from 30 μm to 1500 μm. The influence of laser scanning strategy on residual stress distribution and distortion magnitude of the bridges has shown that orthogonal scanning pattern between two neighboring block layers is beneficial for reducing part distortion.

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Figures

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Fig. 1

(a) L-shaped bar dimensions and the cross section for residual stress measurement [26], (b) bridge structure dimensions, and (c) schematic showing distortion magnitude of the bridge structure (representative of curling angle of the bridge structure after substrate removal), adapted from Ref. [11]

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Fig. 2

Temperature-dependent material properties of solid materials stainless steel 316 L and Ti-6Al-4V used in the simulations [26,3032]

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Fig. 3

(a) Temperature history output in the microscale laser scan model and (b) equivalent heat source model in the mesoscale hatch model

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Fig. 4

(a) Mesh design and (b) boundary conditions in the macroscale part model

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Fig. 5

Scanning patterns of the macroscale part model: (a) top view and (b) 3D view

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Fig. 6

Thermal history of the bridge structure when sliced into different layers: (a) 5 layers, (b) 10 layers, (c) 15 layers, and (d) 20 layers

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Fig. 7

Cross section views of predicted residual stress contours in building direction (z = 15 mm from the top surface) of the L-shaped bar sliced into 10 (a), 20 (b), 30 (c), 40 (d) block layers; (e) prediction by Hodge et al. [34]; and (f) experimental data measured by Wu et al. [26]

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Fig. 8

Predicted residual stress plots and experimental validation in building direction (z = 4 mm from the top surface) of the L-shaped bar sliced into 40 block layers: (a) node paths 1, 2, and 3 along the X direction and (b) node paths 4 and 5 along the Y direction. Experiment data adapted from Ref. [26].

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Fig. 9

Predicted residual stress accumulation in building direction (σzz) of the L-shaped bar with: (a) 10, (b) 20, and (c) 40 block layers deposited; (d) part removed from the substrate

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Fig. 10

Residual stress distribution of the bridge structure sliced into 20 layers: before substrate removal ((a), (c), (e), (g)); after substrate removal ((b), (d), (f), (h))

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Fig. 11

(a) Curling angle α calculation of the bridge structure sliced in 20 layers after substrate removal; (b) comparison of predicted distortion angles with the experimental data [11]; and (c) curling angle α as a function of element number in the FEA model for the 20 block layer slicing

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Fig. 12

(a) Curling angle α calculation of the bridge structure sliced in 20 layers after substrate removal; (b) comparison of predicted distortion angles with the experimental data [11]; and (c) curling angle α as a function of element number in the FEA model for the 20 block layer slicing

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