In this paper, a user-friendly software, CASAC-ANA, for material data analysis and property prediction is presented. In CASAC-ANA, there are seven methods: Nonlinear Mapping (NLM), Principal Component Analysis (PCA), Stepwise Discriminant Analysis (SDA), Discriminant Analysis with Constellation Graph (DACG), Hierarchical Clustering Analysis (HCA), Stepwise Multiple Linear Regression (SMLR), and Artificial Neural Networks (ANN).
The software has some noteworthy features: (1) only one input file is needed and multipath output is produced; (2) both quantitative and qualitative data of dependent variables are accepted; and (3) it is easy to link with materials property databases. As a generalized modeling tool, CASAC-ANA can be used to treat material data concerning composition, technological processes, properties, and to predict properties of materials.
The validity of the CASAC-ANA software has been tested successfully with three typical case studies concerning structural alloy steels, nickel-base superalloys, and continuously cast copper alloys. These CASAC-ANA methods have been compared and discussed.
Author Information
Zhou, J
Laboratory of Computer Chemistry (LCC), Institute of Chemical Metallurgy, Chinese Academy of Sciences, Beijing, China
Xie, Q
Laboratory of Computer Chemistry (LCC), Institute of Chemical Metallurgy, Chinese Academy of Sciences, Beijing, China
Feng, J
Laboratory of Computer Chemistry (LCC), Institute of Chemical Metallurgy, Chinese Academy of Sciences, Beijing, China
Li, S
Laboratory of Computer Chemistry (LCC), Institute of Chemical Metallurgy, Chinese Academy of Sciences, Beijing, China
Xu, Z
Laboratory of Computer Chemistry (LCC), Institute of Chemical Metallurgy, Chinese Academy of Sciences, Beijing, China
Chen, L
Chinese Data Center of Aeromaterials (CDCAM), Beijing Institute of Aeronautical Materials, Beijing, China
Gui, Z
Chinese Data Center of Aeromaterials (CDCAM), Beijing Institute of Aeronautical Materials, Beijing, China
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