Research Papers

Nanoparticle Sintering Model: Simulation and Calibration Against Experimental Data

[+] Author and Article Information
Obehi G. Dibua

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: ogodibua@utexas.edu

Anil Yuksel

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: anil.yuksel@utexas.edu

Nilabh K. Roy

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: nilabh.roy@utexas.edu

Chee S. Foong

NXP Semiconductors,
Austin, TX 78735
e-mail: cs.foong@nxp.com

Michael Cullinan

Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712
e-mail: michael.cullinan@austin.utexas.edu

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MICRO-AND NANO-MANUFACTURING. Manuscript received June 7, 2018; final manuscript received September 24, 2018; published online November 19, 2018. Assoc. Editor: Marriner Merrill.

J. Micro Nano-Manuf 6(4), 041004 (Nov 19, 2018) (9 pages) Paper No: JMNM-18-1017; doi: 10.1115/1.4041668 History: Received June 07, 2018; Revised September 24, 2018

One of the limitations of commercially available metal additive manufacturing (AM) processes is the minimum feature size most processes can achieve. A proposed solution to bridge this gap is microscale selective laser sintering (μ-SLS). The advent of this process creates a need for models which are able to predict the structural properties of sintered parts. While there are currently a number of good SLS models, the majority of these models predict sintering as a melting process which is accurate for microparticles. However, when particles tend to the nanoscale, sintering becomes a diffusion process dominated by grain boundary and surface diffusion between particles. As such, this paper presents an approach to model sintering by tracking the diffusion between nanoparticles on a bed scale. Phase field modeling (PFM) is used in this study to track the evolution of particles undergoing sintering. Changes in relative density are then calculated from the results of the PFM simulations. These results are compared to experimental data obtained from furnace heating done on dried copper nanoparticle inks, and the simulation constants are calibrated to match physical properties.

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Fig. 1

The evolution of a one by one micrometer bed with 43 particles: (a) initial, (b) 20,000 timesteps (0.16 h), (c) 60,000 timesteps (0.47 h), (d) 100,000 timesteps (0.79 h), (e) 280,000 timesteps (2.2 h), (f) 450,000 timesteps (3.6 h), (g) 700,000 timesteps (5.5 h), (h) 850,000 timesteps (6.7 h), and (i) 1,100,000 timesteps (8.7 h)

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Fig. 2

Densification in the center of the simulation bed after (a) 0 timesteps, (b) 80,000 timesteps, (c) 160,000 timesteps, and (d) 480,000 timesteps

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Fig. 3

Relative density curve, with error bounds, derived from data analysis done on the sintering simulation

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Fig. 4

Experimental procedure: (a) copper nanoparticle ink, (b) dried ink, (c) scraped off dried flakes, (d) pellets in crucible before sintering, and (e) pellets in crucible after sintering

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Fig. 5

Scanning electron microscope images of sintered nanoparticles: (a) before sintering and (b) after sintering

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Fig. 6

Experimental data and curve fit at (a) 450 °C, (b) 500 °C, (c) 550 °C, and (d) 600 °C

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Fig. 7

Consolidation of experiment fit plots

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Fig. 8

Distribution of the average initial density of the simulation and experiment for 12 simulation beds and 24 experiment samples

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Fig. 9

Comparing experimental fit to simulations for (a) 450 °C, (b) 500 °C, and (c) 550 °C

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Fig. 10

The evolution of a two-by-two micrometer bed with 134 particles: (a) initial, (b) 200,000 timesteps (5.1 h), (c) 600,000 timesteps (15 h), (d) 1,000,000 timesteps (26 h), (e) 1,500,000 timesteps (38 h), and (f) 2,000,000 timesteps (51 h)

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Fig. 11

Relative change in density curve derived from data analysis done on a 2-by-2 micrometer bed and the prediction from a 1-by-1 micrometer bed



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