@inproceedings{1517942433a9455bbf579ad2462e5485,
title = "Martensite start temperature predictor for steels using ensemble data mining",
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.",
keywords = "Ensemble learning, Materials informatics, Steel, Supervised learning",
author = "Ankit Agrawal and Abhinav Saboo and Wei Xiong and Greg Olson and Alok Choudhary",
note = "Funding Information: This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHi-MaD). Partial support is also acknowledgedfrom DOE awards DE-SC0014330, DE-SC0019358. Publisher Copyright: {\textcopyright} 2019 IEEE.; 6th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019 ; Conference date: 05-10-2019 Through 08-10-2019",
year = "2019",
month = oct,
doi = "10.1109/DSAA.2019.00067",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "521--530",
editor = "Lisa Singh and {De Veaux}, Richard and George Karypis and Francesco Bonchi and Jennifer Hill",
booktitle = "Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019",
address = "United States",
}