Disclaimer: The results from this tool are estimates based on data consisting of a set of experimental measurements. All results are provided for informational purposes only, in furtherance of the developers' educational mission, to complement the knowledge of materials scientists and engineers, and assist them in their search for new materials with desired properties. The developers may not be held responsible for any decisions based on this tool.
Welcome to the steel fatigue strength predictor. This tool is built using experimental data of more than 400 steels, available from the Japan National Institute for Materials Science (NIMS) MatNavi database. This database consists of processing and composition information of steels, along with the experimentally measured rotating bending fatigue strength (10 million cycles). Various data analytics techniques such as feature selection and regression modeling were used to obtain highly accurate fatigue strength prediction models using a small non-redundant set of six composition parameters and three processing parameters.
The tool estimates the fatigue strength of the steel based on the composition and processing parameters entered by the user. To obtain the fatigue strength prediction, please enter the attribute values below, and click the submit button.
Developer team: Ankit Agrawal, Alok Choudhary
We are grateful to NIMS for making the raw data on fatigue steel strength publicly available, and also to the authors of reference [3] to preprocess the raw NIMS data and make it available as supplementary data accompanying reference [4]. 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 (CHiMaD). The authors also acknowledge partial support from AFOSR award FA9550-12-1-0458.
[1] A. Agrawal and A. Choudhary, "An online tool for predicting fatigue strength of steel alloys based on ensemble data mining," International Journal of Fatigue, vol. 113, pp. 389–400, 2018. [url]
[2] A. Agrawal and A. Choudhary, "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, pp. 2497–2500. [url]
[3] B. P. Gautham, R. Kumar, S. Bothra, G. Mohapatra, N. Kulkarni and K. A. Padmanabhan, "More Efficient ICME through Materials Informatics and Process Modeling," Proceedings of the 1st World Congress on Integrated Computational Materials Engineering (ICME), 2011. [url]
[4] A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary, and S. R. Kalidindi, “Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters,” Integrating Materials and Manufacturing Innovation, vol. 3, no. 8, pp. 1–19, 2014. [url]
Center for Ultra-scale Computing and Information Security (CUCIS), EECS Department, Northwestern University, Evanston, IL 60208, USA