Detecting 3D Spatial Clustering of Particles in Nanocomposites Based on Cross-Sectional Images

Abstract

Metal matrix nanocomposites (MMNCs) are high-strength and lightweight materials with great potential in automotive, aerospace, and many other industries. A uniform distribution of nanoparticles in the metal matrix is critical for achieving high-quality MMNCs; hence, nonuniformity of the particle distribution in MMNCs needs to be detected for quality improvement. For this purpose, this article investigates the problem of three-dimensional (3D) clustering detection based on statistical modeling and analysis of the number of nanoparticles on microscopic cross-sectional images of MMNC specimens. Under a 3D distributional model, the probability distributions of the number of particles on an image under both uniform and nonuniform nanoparticle distributions are derived. Based on the results, a hypothesis test is proposed for detecting the existence of clustering. The performance of the method under various parameter settings is investigated. Finally, the method is applied to images from a real MMNC fabrication process. This article has supplementary material available online.

• Clustering detection
• Hypothesis testing
• Metal matrix nanocomposites (MMNCs)
• Particle distribution

ACKNOWLEDGMENTS

This work is supported by the National Science Foundation grants #0926084 and #1161077, an ECS grant (No. 138213) from the Hong Kong Research Grants Council, and a grant from City University of Hong Kong (No. 7200316). The authors thank the editor, the associate editor, and two referees for their helpful comments that have led to improvements in the article.