Martensite start temperature predictor for steels using ensemble data mining

Ankit Agrawal*, Abhinav Saboo, Wei Xiong, Greg Olson, Alok Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Martensite start temperature (MsT) is an important characteristic of steels, knowledge of which is vital for materials engineers to guide the structural design process of steels. It is defined as the highest temperature at which the austenite phase in steel begins to transform to martensite phase during rapid cooling. Here we describe the development and deployment of predictive models for MsT, given the chemical composition of the material. The data-driven models described here are built on a dataset of about 1000 experimental observations reported in published literature, and the best model developed was found to significantly outperform several existing MsT prediction methods. The data-driven analyses also revealed several interesting insights about the relationship between MsT and the constituent alloying elements of steels. The most accurate predictive model resulting from this work has been deployed in an online web-tool that takes as input the elemental alloying composition of a given steel and predicts its MsT. The online MsT predictor is available at http://info.eecs.northwestern.edu/MsTpredictor.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
EditorsLisa Singh, Richard De Veaux, George Karypis, Francesco Bonchi, Jennifer Hill
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages521-530
Number of pages10
ISBN (Electronic)9781728144931
DOIs
StatePublished - Oct 2019
Event6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 - Washington, United States
Duration: Oct 5 2019Oct 8 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019

Conference

Conference6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019
CountryUnited States
CityWashington
Period10/5/1910/8/19

Keywords

  • Ensemble learning
  • Materials informatics
  • Steel
  • Supervised learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Agrawal, A., Saboo, A., Xiong, W., Olson, G., & Choudhary, A. (2019). Martensite start temperature predictor for steels using ensemble data mining. In L. Singh, R. De Veaux, G. Karypis, F. Bonchi, & J. Hill (Eds.), Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 (pp. 521-530). [8964188] (Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2019.00067