TY - GEN
T1 - A fatigue strength predictor for steels using ensemble data mining
AU - Agrawal, Ankit
AU - Choudhary, Alok
N1 - Funding Information:
We are grateful to NIMS for making the Mat Navi database [3] publicly available, and also to the authors of reference [6] for preprocessing the raw NIMS data and making it publicly available as supplementary data accompanying reference [5]. This work was performed under the following financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHi-MaD). The authors also acknowledge partial support from AFOSR award FA9550-12-1-0458.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - Fatigue strength is one of the most important mechanical properties of steel. High cost and time for fatigue testing, and potentially disastrous consequences of fatigue failures motivates the development of predictive models for this property. We have developed advanced data-driven ensemble predictive models for this purpose with an extremely high cross-validated accuracy of >98%, and have deployed these models in a user-friendly online web-tool, which can make very fast predictions of fatigue strength for a given steel represented by its composition and processing information. Such a tool with fast and accurate models is expected to be a very useful resource for the materials science researchers and practitioners to assist in their search for new and improved quality steels. The web-tool is available at http://info.eecs.northwestern.edu/SteelFatigueStrengthPredictor.
AB - Fatigue strength is one of the most important mechanical properties of steel. High cost and time for fatigue testing, and potentially disastrous consequences of fatigue failures motivates the development of predictive models for this property. We have developed advanced data-driven ensemble predictive models for this purpose with an extremely high cross-validated accuracy of >98%, and have deployed these models in a user-friendly online web-tool, which can make very fast predictions of fatigue strength for a given steel represented by its composition and processing information. Such a tool with fast and accurate models is expected to be a very useful resource for the materials science researchers and practitioners to assist in their search for new and improved quality steels. The web-tool is available at http://info.eecs.northwestern.edu/SteelFatigueStrengthPredictor.
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U2 - 10.1145/2983323.2983343
DO - 10.1145/2983323.2983343
M3 - Conference contribution
AN - SCOPUS:84996561740
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2497
EP - 2500
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Y2 - 24 October 2016 through 28 October 2016
ER -