9Cr steel visualization and predictive modeling

Journal Article · Sat Mar 23 00:00:00 EDT 2019 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2019.03.015· OSTI ID:1607775

Krishnamurthy, Narayanan ^[1]; Maddali, Siddharth ^[2]; Hawk, Jeffrey A. ^[3]; Romanov, Vyacheslav N. ^[1]

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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)

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.

• Research Organization: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy (FE)
• OSTI ID: 1607775
• Alternate ID(s): OSTI ID: 1778336
• Journal Information: Computational Materials Science, Vol. 168, Issue C; ISSN 0927-0256
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
data processing steps
clustering
materials strength
feature engineering
predictive modeling