Research Papers

A Combined Experimental-Numerical Method to Evaluate Powder Thermal Properties in Laser Powder Bed Fusion

[+] Author and Article Information
Bo Cheng

Industrial Engineering Department,
University of Louisville,
Louisville, KY 40292
e-mail: bcheng1@crimson.ua.edu

Brandon Lane

Engineering Laboratory,
National Institute of Standards and Technology,
Gaithersburg, MD 20899
e-mail: brandon.lane@nist.gov

Justin Whiting

Engineering Laboratory,
National Institute of Standards and Technology,
Gaithersburg, MD 20899
e-mail: justin.whiting@nist.gov

Kevin Chou

Industrial Engineering Department,
University of Louisville,
Louisville, KY 40292
e-mail: kevin.chou@louisville.edu

1Corresponding author.

Manuscript received June 27, 2018; final manuscript received July 6, 2018; published online August 22, 2018. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 140(11), 111008 (Aug 22, 2018) (8 pages) Paper No: MANU-18-1492; doi: 10.1115/1.4040877 History: Received June 27, 2018; Revised July 06, 2018

Powder bed metal additive manufacturing (AM) utilizes a high-energy heat source scanning at the surface of a powder layer in a predefined area to be melted and solidified to fabricate parts layer by layer. It is known that powder bed metal AM is primarily a thermal process, and further, heat conduction is the dominant heat transfer mode in the process. Hence, understanding the powder bed thermal conductivity is crucial to process temperature predictions, because powder thermal conductivity could be substantially different from its solid counterpart. On the other hand, measuring the powder thermal conductivity is a challenging task. The objective of this study is to investigate the powder thermal conductivity using a method that combines a thermal diffusivity measurement technique and a numerical heat transfer model. In the experimental aspect, disk-shaped samples, with powder inside, made by a laser powder bed fusion (LPBF) system, are measured using a laser flash system to obtain the thermal diffusivity and the normalized temperature history during testing. In parallel, a finite element (FE) model is developed to simulate the transient heat transfer of the laser flash process. The numerical model was first validated using reference material testing. Then, the model is extended to incorporate powder enclosed in an LPBF sample with thermal properties to be determined using an inverse method to approximate the simulation results to the thermal data from the experiments. In order to include the powder particles' contribution in the measurement, an improved model geometry, which improves the contact condition between powder particles and the sample solid shell, has been tested. A multipoint optimization inverse heat transfer method is used to calculate the powder thermal conductivity. From this study, the thermal conductivity of a nickel alloy 625 powder in powder bed conditions is estimated to be 1.01 W/m K at 500 °C.

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Fig. 1

A typical configuration of a laser flash system

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Fig. 2

Illustration of normalized output curve from thermal signals

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Fig. 3

DLF-1600 laser flash system

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Fig. 4

Example of raw and normalized data curves from single-shot laser

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Fig. 5

Model mesh and boundary conditions

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Fig. 6

Solid molybdenum material properties3

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Fig. 7

Simulation versus experimental results at different testing temperatures (a) 94.5 °C, (b) 798.6 °C

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Fig. 8

Comparisons of measured thermal diffusivity with literature data [20] used in simulations. Error bars are standard uncertainty from Mo calibration material certificate.

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Fig. 9

Dimensional information (in mm) of the model for fabrication of powder-enclosed samples

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Fig. 10

Physical properties of solid 17-4 stainless steel [21,22]

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Fig. 11

Normalized thermal response curves from simulation and experiment for flat-surfaced powder-enclosed samples: (a) 95 °C and (b) 300.4 °C

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Fig. 12

Cross-sectional view of new sample design showing internal cones (unit: mm)

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Fig. 13

(a) DLF-1200 laser flash apparatus and (b) normalized curves from testing of both solid and powder-enclosed samples

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Fig. 14

(a) Sample holder and sample for testing and (b) dimensional information of sample holder (mm)

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Fig. 15

Optimization process flow for unknown estimation

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Fig. 17

Result from powder-enclosed sample analysis (500 °C): (a) comparison between different iterations and (b) final simulation result comparison with experiment

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Fig. 16

Normalized temperature versus time for solid sample analysis to obtain the contact conductance between the sample and the sample holder



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