Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels

Verma, Amit K.; Hawk, J.A.; Bruckman, Laura S.; French, Roger H.; Romanov, Vyacheslav; Carter, Jennifer L. W.

doi:10.1007/s11661-019-05234-9

Cited by 28 publications

(16 citation statements)

References 51 publications

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“…Ni (41.7-86), Cr (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), Al (0-3.9), Co (0-16.2), Fe (0-40.1), Mn (0-1.5), Mo (0-9.8), Si (0-0.5), Ti (0-3.3), Y (0-0.1) and Fe in the dry and wet air (s-k p ) datasets, as shown in Supplementary Fig. 1.…”

Section: Compositions Of Model Alloysmentioning

confidence: 99%

“…Recently, data analytics approaches have been successfully applied to predict the mechanical properties of multi-component high-temperature alloys 18,[22][23][24][25][26][27] . While high-temperature oxidation also has scientific and practical importance, to the best of the authors' knowledge, very limited effort has been made to predict the oxidation kinetics of complex multi-component alloys by ML.…”

Section: Introductionmentioning

confidence: 99%

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Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

et al. 2021

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Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants (kp). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.

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Section: Compositions Of Model Alloysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

et al. 2021

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“…The raw experimental dataset was compiled by National Energy Technology Laboratory 8,9 , USA, using the creep datasheet for high Cr steel 39 in the MatNavi materials database by the National Institute for Materials Science, Japan. The dataset is consists of compositions of 18 elements, processing and testing temperatures, and PAGS (converted from austenite grain size number).…”

Section: Experimental Dataset and Synthetic Alloy Features Via Thermomentioning

confidence: 99%

“…The majority of previous efforts applying ML to predict the properties of high-temperature alloys have used alloy compositions and simple processing conditions as features [6][7][8][9][10][11][12][13] . While these approaches can leverage experimental data accumulated over decades, extrapolating (and even interpolating) these models outside the range of the input data is risky due to the absence of physical constraints.…”

Section: Introductionmentioning

confidence: 99%

Coupling physics in machine learning to predict properties of high-temperatures alloys

et al. 2020

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High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

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“…This continues to be an ongoing and evolving process as internal testing has continued to add property information to the database as well as ongoing efforts to pull data into the database from less accessible external sources. This database has been used in many analytical efforts to draw conclusions using data on the effect of composition (actual chemistry of the major elements as well as the minor ones down to parts per million), properties (static and dynamic where existing), and general microstructure features on material behavior for general alloy classes (Ref [5][6][7][8][9][10][11]. To improve understanding and prediction based on the given data, the results of these past analyses, therefore, should be included and expanded upon in later works.…”

Section: Introductionmentioning

confidence: 99%

Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction

Wenzlick

Mamun

Devanathan

et al. 2021

J. of Materi Eng and Perform

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Data analytics methods have been increasingly applied to understanding materials chemistry, processing due to the manufacturing approach, and uni-axial and cyclic property relationships in the highly complex space of alloy design. There are several benefits to applying data analytics to this space, including the ability to manage non-linearities in the responses of the alloy attributes and the resulting mechanical properties. However, key difficulties in applying and understanding the results of data analytics include the often lack of reported assumptions and data processing steps necessary to improve interpretation and reproducibility in derived results. In this work, the methods used to generate clustering and correlation analyses for experimental 9% Cr ferritic-martensitic steel data were investigated and the resulting implications for mechanical property predictions were assessed. This work uses principal component analysis, partitioning around medoids, t-SNE, and k-means clustering to investigate trends in composition, processing and microstructure information with creep and tensile properties, building on work done previously using a smaller version of the same dataset. The initial assumptions, preprocessing steps and methods are investigated and outlined in order to depict the fine level of detail required to convey the steps taken to process data and produce analytical results. The variations in the resulting analyses are explored due to the influence of new and more varied data.

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Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels

Cited by 28 publications

References 51 publications

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Coupling physics in machine learning to predict properties of high-temperatures alloys

Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction

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