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Title: 9Cr steel visualization and predictive modeling

Abstract

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increasemore » strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.« less

Authors:
 [1];  [2];  [3];  [1]
  1. National Energy Technology Lab. (NETL), Pittsburgh, PA (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
  3. National Energy Technology Lab. (NETL), Albany, OR (United States)
Publication Date:
Research Org.:
National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1607775
Alternate Identifier(s):
OSTI ID: 1778336
Resource Type:
Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 168; Journal Issue: C; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; data processing steps; clustering; materials strength; feature engineering; predictive modeling

Citation Formats

Krishnamurthy, Narayanan, Maddali, Siddharth, Hawk, Jeffrey A., and Romanov, Vyacheslav N. 9Cr steel visualization and predictive modeling. United States: N. p., 2019. Web. doi:10.1016/j.commatsci.2019.03.015.
Krishnamurthy, Narayanan, Maddali, Siddharth, Hawk, Jeffrey A., & Romanov, Vyacheslav N. 9Cr steel visualization and predictive modeling. United States. https://doi.org/10.1016/j.commatsci.2019.03.015
Krishnamurthy, Narayanan, Maddali, Siddharth, Hawk, Jeffrey A., and Romanov, Vyacheslav N. Sat . "9Cr steel visualization and predictive modeling". United States. https://doi.org/10.1016/j.commatsci.2019.03.015. https://www.osti.gov/servlets/purl/1607775.
@article{osti_1607775,
title = {9Cr steel visualization and predictive modeling},
author = {Krishnamurthy, Narayanan and Maddali, Siddharth and Hawk, Jeffrey A. and Romanov, Vyacheslav N.},
abstractNote = {The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.},
doi = {10.1016/j.commatsci.2019.03.015},
journal = {Computational Materials Science},
number = C,
volume = 168,
place = {United States},
year = {Sat Mar 23 00:00:00 EDT 2019},
month = {Sat Mar 23 00:00:00 EDT 2019}
}

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Cited by: 8 works
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Works referencing / citing this record:

Data Assessment Method to Support the Development of Creep-Resistant Alloys
journal, January 2020

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