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Materials data analytics for 9% Cr family steel
Author(s) -
Romanov Vyacheslav N.,
Krishnamurthy Narayanan,
Verma Amit K.,
Bruckman Laura S.,
French Roger H.,
Carter Jennifer L.W.,
Hawk Jeffrey A.
Publication year - 2019
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11406
Subject(s) - data mining , computer science , big data , ultimate tensile strength , data set , materials science , artificial intelligence , metallurgy
A materials data analytics (MDA) methodology was developed in this study to evaluate publicly available information on 9% Cr family steel and to handle nonlinear relationships and the sparsity in materials data for this alloy class. The overarching goal is to accelerate the design process as well as to reduce the time and expense associated with qualification testing of new alloys for fossil energy applications. Data entries in the analyzed data set for 82 iron‐base alloy compositions, several processing parameters, and results of tensile mechanical tests selected for this study were arranged in 34 columns by 915 rows. While detailed microstructural information was not available, it is assumed that the compositional space for the 9 to 12% Cr steels is limited such that all data entries have a tempered martensitic microstructure during service. Establishing a hierarchy of first‐order trends in the publicly available data requires the MDA to filter out the biases. Complexity of the phase transformations and microstructure evolution in the multicomponent alloys (using 21 chemical elements) with major influence on mechanical properties, leads to inefficiency in direct application of unbiased linear regression across the entire data space. To address the nonlinearity, analyses of tensile data were performed in composition‐based clusters. Clusters corresponding to moderately frequent patterns and maximized information gain were further refined by using p ‐norm distance measures, matching the alloy classification groups adopted by industry. The evolutionary method of propagating an ensemble of competing cluster‐based models proved to be a viable option in dealing with scarce, multidimensional data.