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Research Papers

Comparison of Static and Dynamic Powder Compaction: Experiment and Simulation

[+] Author and Article Information
C. A. Braun, M. Schumaker

Department of Mechanical Engineering,
Marquette University,
1515 W. Wisconsin Avenue,
Milwaukee, WI 53233

J. Rice

Associate Professor
Department of Mechanical Engineering,
Marquette University,
1515 W. Wisconsin Avenue,
Milwaukee, WI 53233

J. P. Borg

Associate Professor
Mem. ASME
Department of Mechanical Engineering,
Marquette University,
1515 W. Wisconsin Avenue,
Milwaukee, WI 53233
e-mail: john.borg@mu.edu

1Corresponding author.

Contributed by the Materials Division of ASME for publication in the JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY. Manuscript received February 3, 2015; final manuscript received September 8, 2015; published online October 8, 2015. Assoc. Editor: Curt Bronkhorst.

J. Eng. Mater. Technol 138(1), 011003 (Oct 08, 2015) (12 pages) Paper No: MATS-15-1031; doi: 10.1115/1.4031615 History: Received February 03, 2015; Revised September 08, 2015

In this work, the static and dynamic compaction response of a six-material mixture, containing both brittle and ductile constituents, is compared. Quasi-static and dynamic compaction experiments were conducted on samples and the results compared to simulations. Optical analyses of compacted samples indicate that dynamically compacting samples to near 300 m/s is not sufficient for complete compaction or localized grain melt. Simulations indicate that a wide distribution of temperature and stress states are achieved in the dynamically compacted samples; compaction speeds should be increased to near 800 m/s at which point copper grains achieve melt temperatures on their surfaces. The experimental data is used to fit a bulk P-α equation of state (EOS) that can be used for simulating large-scale dynamic compaction for industrial applications.

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References

Figures

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Fig. 1

Schematic of quasi-static test cell for use with 810 MTS assembly

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Fig. 2

Schematic of dynamic test cell. Thicknesses are listed in Table 1.

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Fig. 3

Computational domain where particles have been shaded by material type: copper (smallest particles), graphite (second largest particles), silica (largest particles), iron (second smallest particles), and the aluminum front and back plate

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Fig. 4

Comparison of (a) incident and (b) transmitted gage data for shot velocity 263 m/s and powder density 2.724 g/cm3

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Fig. 5

Comparison of states achieved from the quasi-static and dynamic tests as well as three-dimensional simulated results. (a) The P-α curve has been fit for comparison. (b) Phase diagram showing melt and Hugoniot for pure copper. Copper powder data has been included for comparison [30].

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Fig. 6

Two-dimensional section of the longitudinal stress at two instances corresponding to the initial shock and reshock for an impact velocity of 263 m/s. (a) Longitudinal stress after initial shock at 4.6 μs and (b) longitudinal stress after reshock at 5.6 μs.

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Fig. 7

Two-dimensional section of the temperature field at two instances in time corresponding to the initial shock and reshock for an impact velocity of 263 m/s. Higher temperatures are concentrated along the contact surfaces of particles where the material impedance mismatch is largest. (a) Temperature after initial shock at 4.6 μs and (b) temperature after reshock at 5.6 μs.

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Fig. 8

Two-dimensional cross section of the temperature (left) and pressure (right) at 0.9 μs   (the Hugoniot state) for an impact velocity of 800 m/s. The material cross section is the same as that presented in Fig. 7.

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Fig. 9

Simulated stress and temperature distributions at 4.6 μs for a portion of the powder at the Hugoniot state for an impact velocity of 263 m/s. (a) Stress distribution and (b) temperature distribution.

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Fig. 10

Simulated stress and temperature distributions at 5.6 μs for the same portion of the powder presented in Fig. 8 immediately after reshock for an impact velocity of 263 m/s. (a) Stress distribution and (b) temperature distribution.

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Fig. 11

Simulated stress and temperature distributions of a portion of the powder at the Hugoniot state for an impact velocity of 800 m/s. (a) Stress distribution and (b) temperature distribution.

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Fig. 12

Optical images of a cross section of the mixture after statically loaded to 0.275 GPa and dynamically loaded to 0.365 GPa. Loading is from left to right illustrating the longitudinal direction of loading of the samples. (a) Statically compacted and (b) dynamically compacted.

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Fig. 13

Optical images of mixture statically loaded to 0.275 GPa and dynamically loaded to 0.365 GPa, with loading from left to right. Large silica particles fracture in the direction of loading. (a) Statically compacted and (b) dynamically compacted.

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Fig. 14

SEM images of mixture statically loaded to 0.275 GPa and dynamically loaded to 0.365 GPa, with loading left to right. (a) Statically compacted and (b) dynamically compacted.

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Fig. 15

SEM Image of dynamically compressed samples at two different amplitudes. The degree of bonding is evident. (a) Peak pressure 0.280 GPa and (b) peak pressure 0.452 GPa.

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Fig. 16

Measured rise time–stress relationship for aluminum and mixture

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