Data Analytics for Alloy Qualification

Krishnamurthy, Narayanan; Maddali, Siddharth; Verma, Amit K.; Bruckman, Laura S.; Carter, Jennifer; French, Roger H.; Romanov, Vyacheslav; Hawk, Jeffrey A.

doi:10.2172/1456238

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…The 82‐alloys dataset for the demonstration of CoFi functionality was compiled as part of the U.S. Department of Energy's Advanced Alloy Development program 18 and was later augmented by information about additional 17 alloys. The augmented dataset was most recently used for developing a variety of ML approaches for explaining compositional segmentation, quantifying uncertainty, and others 19,20 …”

Section: Case Studymentioning

confidence: 99%

Convoluted filtering for process cycle modeling

Romanov

2023

Engineering Reports

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Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process‐structure–property‐performance relationships hidden in materials data. The process‐structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process‐structure relations. In this work, a novel convoluted model‐filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe‐based alloy heat treatment cycles. The artifacts were pre‐trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

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Section: Case Studymentioning

confidence: 99%

Convoluted filtering for process cycle modeling

Romanov

2023

Engineering Reports

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“…Data (see Table 1) used in this paper have come from a variety of sources: (a) NETL in-house research; (b) NIMS database [35][36][37][38][39][40][41][42][43]; (c) open literature; and (d) proprietary research [27]. A small subset of carbon steels with similar ferritic or martensitic lath microstructure (and average prior austenite grain size)-typically identified as 9 to 12% Cr (or 9% Cr family for simplicity) ferritic-martensitic steels (iron-chromium alloys with body-centered cubic crystal morphology)-were chosen for these data.…”

Section: Data Management and Analysis Setupmentioning

confidence: 99%

Materials data analytics for 9% Cr family steel

Romanov

Krishnamurthy

Verma

et al. 2019

Statistical Analysis

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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.

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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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Data Analytics for Alloy Qualification

Cited by 8 publications

References 46 publications

Convoluted filtering for process cycle modeling

Convoluted filtering for process cycle modeling

Materials data analytics for 9% Cr family steel

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

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