Detecting 3D spatial clustering of particles in nanocomposites based on cross-sectional images

Qiang Zhou, Junyi Zhou, Michael De Cicco, Shiyu Zhou, Xiaochun Li

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)212-224
Number of pages13
Issue number2
StatePublished - Apr 3 2014


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

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics


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