A Fatigue Strength Predictor for Steels Using Ensemble Data Mining

Ankit Agrawal, Alok Choudhary
2016 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16  
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
more » ... trength 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
doi:10.1145/2983323.2983343 dblp:conf/cikm/AgrawalC16 fatcat:tr5rjwkrebfxpdnreaezvusc5e