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Research Papers: Melting and Solidification

Use of Detailed Particle Melt Modeling to Calculate Effective Melt Properties for Powders

[+] Author and Article Information
Daniel Moser

Department of Mechanical Engineering,
The University of Texas at Austin,
204 E., Dean Keeton Street,
Stop C2200 ETC II 5.160,
Austin, TX 78712
e-mail: danrmoser@utexas.edu

Anil Yuksel, Michael Cullinan

Department of Mechanical Engineering,
The University of Texas at Austin,
204 E., Dean Keeton Street,
Stop C2200 ETC II 5.160,
Austin, TX 78712

Jayathi Murthy

Henry Samueli School of Engineering
and Applied Science,
University of California,
7400 Boelter Hall Los Angeles,
Los Angeles, CA 90095

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received May 10, 2017; final manuscript received August 31, 2017; published online January 17, 2018. Assoc. Editor: Gennady Ziskind.

J. Heat Transfer 140(5), 052301 (Jan 17, 2018) (11 pages) Paper No: HT-17-1264; doi: 10.1115/1.4038423 History: Received May 10, 2017; Revised August 31, 2017

Selective laser melting (SLM) is a widely used powder-based additive manufacturing process. However, it can be difficult to predict how process inputs affect the quality of parts produced. Computational modeling has been used to address some of these difficulties, but a challenge has been accurately capturing the behavior of the powder in a large, bed-scale model. In this work, a multiscale melting model is implemented to simulate the melting of powder particles for SLM. The approach employs a particle-scale model for powder melting to develop a melt fraction–temperature relationship for use in bed-scale simulations of SLM. Additionally, uncertainties from the particle-scale are propagated through the relationship to the bed scale, thus allowing particle-scale uncertainties to be included in the bed-scale uncertainty estimation. Relations, with uncertainty, are developed for the average melt fraction of the powder as a function of the average temperature of the powder. The utility of these melt fraction–temperature relations is established by using them to model phase change using a continuum bed-scale model of the SLM process. It is shown that the use of the developed relations captures partial melt behavior of the powder that a simple melting model cannot. Furthermore, the model accounts for both uncertainty in material properties and packing structure in the final melt fraction–temperature relationship, unlike simple melting models. The developed melt fraction–temperature relations may be used for bed-scale SLM simulations with uncertainty due to particle effects.

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Figures

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Fig. 3

Particle volume fractions on background mesh corresponding to DEM particles. Particles, base plate, and air are included in the simulation.

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Fig. 2

Discrete element method particles placed on background mesh

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Fig. 1

Schematic of SLM process at bed scale, and inset showing particle-scale domain

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Fig. 9

Probability density function of predicted melt track width for stainless steel powder

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Fig. 11

Probability density function of predicted melt pool depth for titanium plate

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Fig. 4

Probability density function of predicted melt pool depth for P = 25 W and speed = 15 cm/s

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Fig. 5

Probability density function of predicted melt pool width for P = 25 W and speed = 15 cm/s

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Fig. 6

Probability density function of predicted melt pool depth for P = 50 W and speed = 30 cm/s

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Fig. 7

Probability density function of predicted melt pool width for P = 50 W and speed = 30 cm/s

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Fig. 8

Probability density function of predicted melt track height for stainless steel powder

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Fig. 12

Probability density function of predicted melt track width for titanium powder

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Fig. 13

Probability density function of predicted melt track height for titanium powder

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Fig. 10

Probability density function of predicted melt pool width for titanium plate

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Fig. 14

Melt pool evolution for stainless steel 175 W, 2 m/s

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Fig. 15

Average melt fraction versus average temperature for solid and powdered material

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Fig. 16

Average melt fraction versus average temperature for domain elements different distances away from laser path

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Fig. 17

Average melt fraction versus average temperature for the same domain element with different packing structures

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Fig. 18

Average melt fraction versus average temperature for the same domain element with different laser powers

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Fig. 19

Average melt fraction versus average temperature for the same domain element with different laser speeds

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Fig. 20

Domain for bed-scale simulations

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Fig. 21

Comparison of solid fraction predictions using: 1—particle-scale melting model with mesh super-imposed; 2—bed-scale powder melting model, in which the gp(T) curve is computed from the particle-scale melt model, 3—bed-scale bulk material melting model, in which gp(T) assumes a bulk material with melting occurring between solidus and liquidus temperatures

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