Quantification of Microstructures using Data Analysis and Machine Learning Methods
Kwang-Ryeol LEE, KIST, South Korea
Veena TIKARE, Sandia National Laboratories, USA (Programme Chair)
Jui-Sheng CHOU, Taiwan Tech, Taiwan
Masahiko DEMURA, NIMS, Japan
Raynald GAUVIN, McGill University, Canada
Robert J. HANISCH, NIST, USA
Satoshi HATA, Kyushu University, Japan
Elizabeth HOLM, Carnegie Mellon University, USA
Surya KALIDINDI, Georgia Institute of Technology, USA
Sergei V. KALININ, ORNL, USA
Bryce MEREDIG, Citrine Informatics, USA
Veera SUNDARARAGHAVAN, University of Michigan, Ann Arbor, USA
Ichiro TAKEUCHI, University of Maryland, USA
Francois WILLOT, MINES ParisTech, France
Aron WALSH, Imperial College London, UK
Chris, WOLVERTON, Northwestern University, USA
Matter of interest:
- Automated application of traditional stereological techniques and their extension to three-dimensional microstructures.
- Application of data analysis techniques such as multi-point statistics, primary component analysis, object-based image analysis, feature identification and extraction, spatial correlation and 3D pose estimation.
- Use of machine learning to identify key features and the correct metrics to quantify their characteristics, find spatial correlations or hierarchical relations and other relationships that are not possible when microstructures are analyzed manually.
- While the focus is on microstructure quantification, works that relate microstructure to either fabrication processes or to engineering properties using data analytics or machine learning will be considered.
CB-1 Automation of stereological techniques to characterize microstructure
CB-2 Data analysis for quantitative description of microstructure (i.e. multi-point methods, primary component analysis, spectral methods, etc.)
CB-3 Machine learning to recognize microstructural features and quantify microstructures
CB-4 Development of long-range descriptors (i.e. spatial correlation, hierarchical relations)
CB-5 Quantitative comparison between different microstructures
CB-6 Application of data analytics or machine learning to find processing-microstructure or microstructure-property relationships