TY - JOUR

T1 - Discovery of marageing steels

T2 - machine learning vs. physical metallurgical modelling

AU - Shen, C.

AU - Wang, C.

AU - Rivera-Díaz-del-Castillo, P.E.J.

AU - Xu, D.

AU - Zhang, Q.

AU - Zhang, C.

AU - Xu, W.

PY - 2021/10/10

Y1 - 2021/10/10

N2 - Physical metallurgical (PM) and data-driven approaches can be independently applied to alloy design. Steel technology is a field of physical metallurgy around which some of the most comprehensive understanding has been developed, with vast models on the relationship between composition, processing, microstructure and properties. They have been applied to the design of new steel alloys in the pursuit of grades of improved properties. With the advent of rapid computing and low-cost data storage, a wealth of data has become available to a suite of modelling techniques referred to as machine learning (ML). ML is being emergingly applied in materials discovery while it requires data mining with its adoption being limited by insufficient high-quality datasets, often leading to unrealistic materials design predictions outside the boundaries of the intended properties. It is therefore required to appraise the strength and weaknesses of PM and ML approach, to assess the real design power of each towards designing novel steel grades. This work incorporates models and datasets from well-established literature on marageing steels. Combining genetic algorithm (GA) with PM models to optimise the parameters adopted for each dataset to maximise the prediction accuracy of PM models, and the results were compared with ML models. The results indicate that PM approaches provide a clearer picture of the overall composition-microstructure-properties relationship but are highly sensitive to the alloy system and hence lack on exploration ability of new domains. ML conversely provides little explicit physical insight whilst yielding a stronger prediction accuracy for large-scale data. Hybrid PM/ML approaches provide solutions maximising accuracy, while leading to a clearer physical picture and the desired properties.

AB - Physical metallurgical (PM) and data-driven approaches can be independently applied to alloy design. Steel technology is a field of physical metallurgy around which some of the most comprehensive understanding has been developed, with vast models on the relationship between composition, processing, microstructure and properties. They have been applied to the design of new steel alloys in the pursuit of grades of improved properties. With the advent of rapid computing and low-cost data storage, a wealth of data has become available to a suite of modelling techniques referred to as machine learning (ML). ML is being emergingly applied in materials discovery while it requires data mining with its adoption being limited by insufficient high-quality datasets, often leading to unrealistic materials design predictions outside the boundaries of the intended properties. It is therefore required to appraise the strength and weaknesses of PM and ML approach, to assess the real design power of each towards designing novel steel grades. This work incorporates models and datasets from well-established literature on marageing steels. Combining genetic algorithm (GA) with PM models to optimise the parameters adopted for each dataset to maximise the prediction accuracy of PM models, and the results were compared with ML models. The results indicate that PM approaches provide a clearer picture of the overall composition-microstructure-properties relationship but are highly sensitive to the alloy system and hence lack on exploration ability of new domains. ML conversely provides little explicit physical insight whilst yielding a stronger prediction accuracy for large-scale data. Hybrid PM/ML approaches provide solutions maximising accuracy, while leading to a clearer physical picture and the desired properties.

KW - Machine learning

KW - Marageing steel

KW - Physical metallurgy

KW - Small sample problem

KW - Alloy steel

KW - Data mining

KW - Digital storage

KW - Forecasting

KW - Genetic algorithms

KW - Microstructure

KW - Data-driven approach

KW - Microstructure and properties

KW - Microstructure properties

KW - Modelling techniques

KW - Physical pictures

KW - Prediction accuracy

KW - Steel technologies

KW - Steel metallurgy

U2 - 10.1016/j.jmst.2021.02.017

DO - 10.1016/j.jmst.2021.02.017

M3 - Journal article

VL - 87

SP - 258

EP - 268

JO - Journal of Materials Science and Technology

JF - Journal of Materials Science and Technology

SN - 1005-0302

IS - 10

ER -