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Machine Learning to Predict the Martensite Start Temperature in Steels
Ferritico, Brinellvagen 85, S-10044 Stockholm, Sweden..
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Applied Process Metallurgy.ORCID iD: 0000-0003-0533-6729
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Physical Metallurgy. Ferritico, Brinellvägen 85, S-10044 Stockholm, Sweden.ORCID iD: 0000-0003-3598-2465
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering. Ferritico, Brinellvägen 85, S-10044 Stockholm, Sweden.ORCID iD: 0000-0003-1102-4342
2019 (English)In: Metallurgical and Materials Transactions. A, ISSN 1073-5623, E-ISSN 1543-1940, Vol. 50A, no 5, p. 2081-2091Article in journal (Refereed) Published
Abstract [en]

The martensite start temperature (M-s) is a critical parameter when designing high-performance steels and their heat treatments. It has, therefore, attracted significant interest over the years. Numerous methodologies, such as thermodynamics-based, linear regression and artificial neural network (ANN) modeling, have been applied. The application of data-driven approaches, such as ANN modeling, or the wider concept of machine learning (ML), have shown limited technical applicability, but considering that these methods have made significant progress lately and that materials data are becoming more accessible, a new attempt at data-driven predictions of the M-s is timely. We here investigate the usage of ML to predict the M-s of steels based on their chemical composition. A database of the M(s)vs alloy composition containing 2277 unique entries is collected. It is ensured that all alloys are fully austenitic at the given austenitization temperature by thermodynamic calculations. The ML modeling is performed using four different ensemble methods and ANN. Train-test split series are used to evaluate the five models, and it is found that all four ensemble methods outperform the ANN on the current dataset. The reason is that the ensemble methods perform better for the rather small dataset used in the present work. Thereafter, a validation dataset of 115 M-s entries is collected from a new reference and the final ML model is benchmarked vs a recent thermodynamics-based model from the literature. The ML model provides excellent predictions on the validation dataset with a root-mean-square error of 18, which is slightly better than the thermodynamics-based model. The results on the validation dataset indicate the technical usefulness of the ML model to predict the M-s in steels for design and optimization of alloys and heat treatments. Furthermore, the agility of the ML model indicates its advantage over thermodynamics-based models for M-s predictions in complex multicomponent steels. (C) The Author(s) 2019

Place, publisher, year, edition, pages
SPRINGER , 2019. Vol. 50A, no 5, p. 2081-2091
National Category
Metallurgy and Metallic Materials
Identifiers
URN: urn:nbn:se:kth:diva-251196DOI: 10.1007/s11661-019-05170-8ISI: 000463991300003Scopus ID: 2-s2.0-85063028004OAI: oai:DiVA.org:kth-251196DiVA, id: diva2:1317705
Note

QC 20190523

Available from: 2019-05-23 Created: 2019-05-23 Last updated: 2019-05-23Bibliographically approved

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Mu, WangzhongOdqvist, JoakimHedström, Peter

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