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research-article

Size Distribution Estimation of 3D Particle Clusters in Metal Matrix Nanocomposites Considering Sampling Bias

[+] Author and Article Information
Jianguo Wu

Department of Industrial, Manufacturing and Systems Engineering University of Texas at El Paso 500 W University Ave, Engineering Building, A-244, TX 79968, USA
jwu2@utep.edu

Yuan Yuan

IBM Research, Singapore 10 Marina Boulevard Singapore Marina Bay Financial Centre Tower 2 SINGAPORE, 18983, SG
polarisyy@gmail.com

Xiaochun Li

Department of Mechanical and Aerospace Engineering, University of California, Los Angeles 48-121G Eng IV, Los Angeles, CA 90095, USA
xcli@seas.ucla.edu

1Corresponding author.

ASME doi:10.1115/1.4036642 History: Received February 20, 2017; Revised April 21, 2017

Abstract

Nanoparticle clustering phenomenon is a critical quality issue in metal matrix nanocomposites manufacturing. Accurate estimation of the 3D cluster size distribution based on 2D cross-section images is essential for quality assessment, quality control, and process optimization. The existing studies often draw conclusions with observable samples, which are inherently biased because large clusters more likely to be intersected by SEM images compared with small ones. This paper takes into account this sampling bias and proposes two statistical approaches, namely, the maximum likelihood estimation (MLE) and the method of moments (MM), to estimate the distribution parameters accurately. Numerical studies and real case study demonstrate the effectiveness and accuracy of the proposed approaches.

Copyright (c) 2017 by ASME
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