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

Summary: [This abstract is based on the authors' 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.

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  • Topics: Statistics
  • Keywords: Clustering, Cluster analysis, Image analysis, Hypothesis testing, Metals
  • Author: Zhou, Qiang; Zhou, Junyi; De Cicco, Michael; Zhou, Shiyu; Li, Xiaochun;
  • Journal: Technometrics