Identification and Optimization of AB2 Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Agarwal A, Pettersson F, Singh A, Kong CS, Saxen H, Rajan K, Iwata S, Chakraborti N
Publisher: TAYLOR & FRANCIS INC
Publication year: 2009
Journal: Materials and Manufacturing Processes
Journal acronym: MATER MANUF PROCESS
Volume number: 24
Issue number: 3
Start page: 274
End page: 281
Number of pages: 8
ISSN: 1042-6914


Abstract

Available data for a large number of AB2 compounds were subjected to a rigorous study using a combination of Principal Component Analysis (PCA) technique, multiobjective genetic algorithms, and neural networks that evolved through genetic algorithms. The identification of various phases and phase-groups were very successfully done using a decision tree approach. Since the variable hyperspaces for the different phases were highly intersecting in nature, a cumulative probability index was defined for the formation of individual compounds, which was maximized along with Pauling's electronegativity difference. The resulting Pareto-frontiers provided further insight into the nature of bonding prevailing in these compounds.


Keywords

AB2 compounds, Data mining, Decision tree, Evolutionary algorithm, Genetic algorithms, Laves phase, Multiobjective optimization, Neural network, Principal component analysis

Last updated on 2019-22-11 at 04:18