Inferring 3D Ellipsoids based on Cross-Sectional Images with Applications to Porosity Control of Additive Manufacturing Jianguo Wu1*, Yuan Yuan2, Haijun Gong3, Tzu-Liang (Bill) Tseng4 1* Department of Industrial Engineering and Management, Peking University, Beijing, China, 100871 E-mail:
[email protected] 2 IBM Research, Singapore 18983 3 Department of Manufacturing Engineering, Georgia Southern University, Statesboro, GA 30458, USA 4 Department of Industrial, Manufacturing and Systems Engineering, University of Texas at El Paso, TX 79968, USA Abstract This paper develops a series of statistical approaches to inferring size distribution, volume number density and volume fraction of 3D ellipsoidal particles based on 2D cross-sectional images. Specifically, this paper first establishes an explicit linkage between the size of ellipsoidal particles and the size of cross-sectional elliptical contours. Then an efficient Quasi-Monte Carlo EM algorithm is developed to overcome the challenge of 3D size distribution estimation based on the established complex linkage. The relationship between the 3D and 2D particle number densities is also identified to estimate the volume number density and volume fraction. The effectiveness of the proposed method is demonstrated through simulation and case studies. KEY WORDS: Ellipsoid Intersection; Quasi-Monte Carlo; Expectation Maximization; Additive manufacturing; Porosity. 1. INTRODUCTION This paper develops a novel inspection method to infer the three-dimensional (3D) size distribution, volume number density (number per unit volume), and volume fraction (volume percentage) of ellipsoidal particles in materials based on 2D cross-sectional microscopic images. Although recent development of measurement technology makes the direct measurement of 3D particles possible, indirect measurement from lower dimension are still widely used for practical reasons.