Metal matrix nanocomposites (MMNCs) are produced by dispersing reinforcing nanoparticles into metal matrix. It is a type of emerging materials with high strength and light weight and draws significant attentions in recent years. If the particles are not well dispersed, they will form particle clusters in the metal matrix. These clusters will detrimentally impact on the final quality of MMNCs. This paper proposes a statistical approach to estimating the parameters of the size distribution of clusters in MMNCs. One critical challenge is that the clusters are distributed in a three-dimensional (3D) space, while the observations we have are two-dimensional (2D) cross-section microscopic images of these clusters. In the proposed approach, we first derived the probability distribution of the observed sizes of the 2D cross sections of the clusters and then a maximum likelihood estimation (MLE) method is developed to estimate the 3D cluster size distribution. Computational efficient algorithms are also established to make computational load manageable. The case studies based on simulation and real observed data are conducted, which demonstrates the effectiveness of the proposed approach.
Skip Nav Destination
Article navigation
February 2013
Research-Article
Inferring the Size Distribution of 3D Particle Clusters in Metal Matrix Nanocomposites
Shiyu Zhou,
Shiyu Zhou
1
e-mail: szhou@engr.wisc.edu
Department of Industrial and Systems Engineering,
Department of Industrial and Systems Engineering,
University of Wisconsin-Madison
,3270 Mechanical Engineering
,1513 University Avenue
,Madison, WI 53706
1Corresponding author.
Search for other works by this author on:
Xiaochun Li
Xiaochun Li
Department of Mechanical Engineering,
e-mail: xcli@engr.wisc.edu
University of Wisconsin-Madison
,1035 Mechanical Engineering
,1513 University Avenue
,Madison, WI 53706
e-mail: xcli@engr.wisc.edu
Search for other works by this author on:
Heping Liu
e-mail: hepingliu@yahoo.com
Shiyu Zhou
e-mail: szhou@engr.wisc.edu
Department of Industrial and Systems Engineering,
Department of Industrial and Systems Engineering,
University of Wisconsin-Madison
,3270 Mechanical Engineering
,1513 University Avenue
,Madison, WI 53706
Xiaochun Li
Department of Mechanical Engineering,
e-mail: xcli@engr.wisc.edu
University of Wisconsin-Madison
,1035 Mechanical Engineering
,1513 University Avenue
,Madison, WI 53706
e-mail: xcli@engr.wisc.edu
1Corresponding author.
Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received December 9, 2011; final manuscript received November 18, 2012; published online January 24, 2013. Assoc. Editor: Yong Huang.
J. Manuf. Sci. Eng. Feb 2013, 135(1): 011013 (9 pages)
Published Online: January 24, 2013
Article history
Received:
December 9, 2011
Revision Received:
November 18, 2012
Citation
Liu, H., Zhou, S., and Li, X. (January 24, 2013). "Inferring the Size Distribution of 3D Particle Clusters in Metal Matrix Nanocomposites." ASME. J. Manuf. Sci. Eng. February 2013; 135(1): 011013. https://doi.org/10.1115/1.4023268
Download citation file:
Get Email Alerts
Related Articles
Size Distribution Estimation of Three-Dimensional Particle Clusters in Metal-Matrix Nanocomposites Considering Sampling Bias
J. Manuf. Sci. Eng (August,2017)
Computational Improvements to Estimating Kriging Metamodel Parameters
J. Mech. Des (August,2009)
Modeling the Thermal Conductivity and Phonon Transport in
Nanoparticle Composites Using Monte Carlo Simulation
J. Heat Transfer (April,2008)
Atomistic-Continuum Modeling of the Mechanical Properties of Silica/Epoxy Nanocomposite
J. Eng. Mater. Technol (January,2012)
Related Proceedings Papers
Related Chapters
Presenting a Channel Estimation Method with Considering the Carrier Frequency Offset Based on Comparative Methods in MIMO-OFDM Systems
International Conference on Computer Technology and Development, 3rd (ICCTD 2011)
A Method of Carrier Synchronization Based on Exit for LDPC Encoded Systems
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
Threat Anticipation and Deceptive Reasoning Using Bayesian Belief Networks
Intelligent Engineering Systems through Artificial Neural Networks