An online tool for predicting fatigue strength of steel alloys based on ensemble data mining-Reference-Cited by-同舟云学术

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An online tool for predicting fatigue strength of steel alloys based on ensemble data mining

Published:2018-08 Issue: Volume:113 Page:389-400
ISSN:0142-1123
Container-title:International Journal of Fatigue
language:en
Short-container-title:International Journal of Fatigue

Author:

Agrawal Ankit^ORCID,Choudhary Alok

Funder

Center for Hierarchical Materials Design

U.S. Department of Commerce

Air Force Office of Scientific Research

National Institute of Standards and Technology

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Mechanics of Materials,General Materials Science,Modelling and Simulation

Reference55 articles.

1. Agrawal A, Choudhary A. A fatigue strength predictor for steels using ensemble data mining. In: Proceedings of 25th ACM international conference on information and knowledge management (CIKM) (Demo); 2016. p. 2497–500.

2. Hey T, Tansley S, Tolle K. The fourth paradigm: data-intensive scientific discovery, microsoft research; 2009. .

3. Materials data science: current status and future outlook;Kalidindi;Ann Rev Mater Res,2015

4. Materials informatics: the materials gene and big data;Rajan;Ann Rev Mater Res,2015

5. Perspective: materials informatics and big data: Realization of the fourth paradigm of science in materials science;Agrawal;APL Mater,2016

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