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Research Papers

Uncertainty Analysis of Solid-Liquid-Vapor Phase Change of a Metal Particle Subject to Nanosecond Laser Heating

[+] Author and Article Information
Yuwen Zhang

e-mail: zhangyu@missouri.edu

P. Frank Pai

Department of Mechanical and Aerospace Engineering,
University of Missouri,
Columbia, MO 65211

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the Journal of Manufacturing Science and Engineering. Manuscript received February 9, 2012; final manuscript received January 29, 2013; published online March 22, 2013. Assoc. Editor: Yong Huang.

J. Manuf. Sci. Eng 135(2), 021009 (Mar 22, 2013) (12 pages) Paper No: MANU-12-1042; doi: 10.1115/1.4023714 History: Received February 09, 2012; Revised January 29, 2013

The effects of the uncertainties of various parameters, including the laser fluence, diameter of metal powder particles, laser pulse width, and the initial temperature of metal particles on solid-liquid-vapor phase change processes of metal particles under nanosecond laser heating are investigated in this paper. A systematic approach of simulating the phase change with uncertain parameters is presented and a sample-based stochastic model is established in order to investigate the influence of different uncertain parameters on the maximum surface temperature of metal particles, the maximum solid-liquid interface location, maximum liquid-vapor interface location, maximum saturation temperature, and maximum recoil pressure and the time needed to reach the maximum solid-liquid interface location. The results show that the mean value and standard deviation of the laser fluence have dominant effects on all output parameters.

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Figures

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Fig. 1

Sample-based stochastic model

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Stochastic convergence analysis of the input parameters

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Stochastic convergence analysis of the standard deviation of the input parameters

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Stochastic convergence analysis of the mean value of the output parameters

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Fig. 6

Stochastic convergence analysis of the standard deviation of the output parameters

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Fig. 7

Typical distribution of the output parameters

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Fig. 8

The IQR of the output parameters with different COVs of the input parameters

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Fig. 9

The IQR of the output parameters with different values and COVs of J

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