Materials data analytics for 9% Cr family steel

Abstract

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.

Convoluted filtering for process cycle modeling

“…DeepFreG LL allows us to transfer non-linear causal features into linear spaces, where they are more likely to be adopted in the subsequent task training rather than being replaced with multi-linear models skewed to spurious correlations among miscellaneous training inputs. 2 Non-linear features of interest in this case study are associated with inflection points (Figure B1) in temperature dependence of steel mechanical properties 6 and with the edge of precipitate formation that indirectly controls the changes in mechanical properties.…”

“…However, the data selection for the other two sub-tasks was conditional on proximity to the edges of highly nonlinear patterns. the objective function with weight-averaging across seven compositional clusters (using "clustering for information gain" algorithm 1,2,17 for segmentation); and the test dataset comprised only the cluster-representative alloys.…”

“…Cooperative carbon-nitrogen effects in defining the carbonitride precipitation edge (Figure B2) were previously noted. 2 The shape of the edge used in this case study was empirically derived from c-IG cluster-dependent linear offset (regarding carbon and nitrogen compositional ratio) of the edge patterns by using piecewise-linear ensemble modeling approach.…”

“…Materials data modelers apply principles of materials science and engineering, physics, mathematics, and information science to extract knowledge and insights from process‐structure–property‐performance relationships hidden in the data. Science‐based artificial intelligence (AI) can facilitate materials design and manufacturing, while keeping it transparent and interpretable in domain science 1–3 …”

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

“…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.…”