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Research Papers

Simulation of Forming Process of Powder Bed for Additive Manufacturing

[+] Author and Article Information
Zhaowei Xiang, Zhenbo Deng, Xiaoqin Mei, Guofu Yin

School of Manufacturing Science and Engineering,
Sichuan University,
No. 24 South Section 1, Yihuan Road,
Chengdu 610065, China

Ming Yin

School of Manufacturing Science and Engineering,
Sichuan University,
No. 24 South Section 1, Yihuan Road,
Chengdu 610065, China
e-mail: mingyin@scu.edu.cn

1Corresponding author.

Manuscript received October 6, 2015; final manuscript received February 16, 2016; published online March 25, 2016. Assoc. Editor: Z.J. Pei.

J. Manuf. Sci. Eng 138(8), 081002 (Mar 25, 2016) (9 pages) Paper No: MANU-15-1509; doi: 10.1115/1.4032970 History: Received October 06, 2015; Revised February 16, 2016

The forming process of powder bed for additive manufacturing (AM) is analyzed and is simplified to three processes, including random packing, layering, and compression. The processes are simulated by using the discrete element method (DEM). First, the particles with monosize, bimodal, and Gaussian size distributions are randomly packed. Then, the packed particles are layered with different thicknesses. Finally, a 20 μm compression is applied on the top surface of the layered powder beds. All the processes are simulated based on the soft sphere model. Packing density and coordination number are calculated to evaluate the packing mesostructure. The results indicate that the packing density and coordination number increase with the layer thickness increasing in the initial packing, and compression can effectively increase the density and coordination number of powder bed and decrease the effect of ranging layer thickness. The results also show that powder bed with monosize distribution initially has the best combination performance. Our research provides a theoretical guide to choosing the layer thickness and size distribution initially of powder bed for AM.

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Figures

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Fig. 1

Schematic of forming process of powder bed for AM

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Fig. 2

Steady morphologies of random packing with different size distributions: (a)–(c) are steady states of monosize, bimodal, and Gaussian distributions, respectively

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Fig. 3

Powder beds with different layer thicknesses for monosize distribution initially (h represents thickness)

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Fig. 4

Powder beds with different layer thicknesses for bimodal distribution initially (h represents thickness)

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Fig. 5

Powder beds with different layer thicknesses for Gaussian distribution initially (h represents thickness)

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Fig. 6

Packing densities and coordination numbers with different layer thicknesses and different size distributions initially: (a) packing density and (b) coordination number

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Fig. 7

Powder beds with different layer thicknesses after 20 μm compression based on the powder beds as shown in Fig. 3, respectively (h represents layer thickness)

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Fig. 8

Powder beds with different thicknesses after 20 μm compression based on the powder beds as shown in Fig. 4, respectively (h represents layer thickness)

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Fig. 9

Powder beds with different thicknesses after 20 μm compression based on the powder beds as shown in Fig. 5, respectively (h represents layer thickness)

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Fig. 10

Packing densities and coordination numbers with different thicknesses and different size distributions initially after 20 μm compression: (a) packing density and (b) coordination number

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Fig. 11

Increased packing density and coordination number: (a) packing density and (b) coordination number

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