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

Metal matrix nanocomposites (MMNCs) are high-strength and light-weight 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 non-uniformity of the particle distribution in MMNCs needs to be detected for quality improvement. For this purpose, this paper investigates the problem of 3D clustering detection based on statistical modeling and analysis of the number of nanoparticles on microscopic cross-sectional images of MMNC specimen. Under a 3D distributional model, the probability distributions of the number of particles on an image under both uniform and non-uniform nanoparticle distributions are derived. Based on the results, a hypothesis test is proposed for detecting the existence of clustering. Performance of the method under various parameter settings is investigated. Finally, the method is applied to images from a real MMNC fabrication process. Biography: Dr. Qiang Zhou is an Assistant Professor at the Department of Systems Engineering and Engineering Management, City University of Hong Kong. He received a B.S. in Automotive Engineering (2005) and a M.S. in Mechanical Engineering (2007) from Tsinghua University, a M.S. in Statistics (2010) and a Ph.D. in Industrial Engineering (2011) at the University of Wisconsin-Madison. His research interests include modeling and analysis of complex engineering systems for the purpose of quality control and productivity improvement, system health prognosis and management, and design and analysis of computer experiments. Information: email: [email protected] Fax: 6777-1434