Journal article
Estimating Size and Number Density of Three-Dimensional Particles Using Truncated Cross-Sectional Data
Journal of manufacturing science and engineering, Vol.144(2), 021002
02/01/2022
DOI: 10.1115/1.4051625
Abstract
The need for estimating three-dimensional (3D) information based on two-dimensional (2D) images has been increasing in numerous fields. It is essential in quality assessment, quality control, and process optimization. However, all the existing methods have not considered the data truncation issue, which is commonly faced in metrology. This paper proposes a new statistical approach to infer size distribution and volume number density (VND) of 3D particles based on 2D cross-sectional images with data truncation considered. In order to estimate the size distribution, a linkage is established between 3D particles and 2D observations with the existence of data truncation. Subsequently, this paper derives the likelihood function of 2D observations and an efficient Monte Carlo expectation-maximization algorithm is developed to estimate the parameters of size distribution. In addition, an explicit relationship between the 3D and 2D particle number densities is established and leveraged to estimate the VND and volume fraction. The effectiveness of the proposed method is demonstrated through both simulation study and real case studies in metal additive manufacturing and metal-matrix nanocomposites manufacturing.
Details
- Title: Subtitle
- Estimating Size and Number Density of Three-Dimensional Particles Using Truncated Cross-Sectional Data
- Creators
- Yuanyuan Gao - Peking UniversityXiaohu Huang - Department of Quantitative Investment, DaCheng Fund, Shenzhen 518000, ChinaChao Wang - University of IowaJianguo Wu - Peking University
- Resource Type
- Journal article
- Publication Details
- Journal of manufacturing science and engineering, Vol.144(2), 021002
- Publisher
- ASME
- DOI
- 10.1115/1.4051625
- ISSN
- 1087-1357
- eISSN
- 1528-8935
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 51875003
- Language
- English
- Date published
- 02/01/2022
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984204072102771
Metrics
24 Record Views