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Research Papers

Physical Modeling for Selective Laser Sintering Process

[+] Author and Article Information
Arash Gobal

Department of Mechanical and Aerospace
Engineering,
University of California,
Davis, CA 95616
e-mail: agobal@ucdavis.edu

Bahram Ravani

Fellow ASME
Professor
Department of Mechanical and Aerospace
Engineering,
University of California,
Davis, CA 95616
e-mail: bravani@ucdavis.edu

1Corresponding authors.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received February 5, 2016; final manuscript received August 12, 2016; published online January 30, 2017. Assoc. Editor: Jan C. Aurich.

J. Comput. Inf. Sci. Eng 17(2), 021002 (Jan 30, 2017) (7 pages) Paper No: JCISE-16-1058; doi: 10.1115/1.4034473 History: Received February 05, 2016; Revised August 12, 2016

The process of selective laser sintering (SLS) involves selective heating and fusion of powdered material using a moving laser beam. Because of its complicated manufacturing process, physical modeling of the transformation from powder to final product in the SLS process is currently a challenge. Existing simulations of transient temperatures during this process are performed either using finite-element (FE) or discrete-element (DE) methods which are either inaccurate in representing the heat-affected zone (HAZ) or computationally expensive to be practical in large-scale industrial applications. In this work, a new computational model for physical modeling of the transient temperature of the powder bed during the SLS process is developed that combines the FE and the DE methods and accounts for the dynamic changes of particle contact areas in the HAZ. The results show significant improvements in computational efficiency over traditional DE simulations while maintaining the same level of accuracy.

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References

Figures

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Fig. 1

Random resistor network of powder particles

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Fig. 2

Equilibrium condition of a particle pair

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Fig. 3

(a) Boundary conditions for calculating shape functions and (b) extended and target regions for calculation of shape functions

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Fig. 4

Smooth transition between FE and DE models

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Fig. 5

Convergence of the average coordination number in particle packing

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Fig. 6

CPU runtime for producing the desired powder bed

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Fig. 7

(a) Breakdown of powder bed into eight-node brick elements and (b) packing of spherical particles into each element

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Fig. 8

Transient temperature during the SLS process

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