Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters

Ankit Agrawal*, Parijat D. Deshpande, Ahmet Cecen, Gautham P. Basavarsu, Alok N. Choudhary, Surya R. Kalidindi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

122 Scopus citations

Abstract

This paper describes the use of data analytics tools for predicting the fatigue strength of steels. Several physics-based as well as data-driven approaches have been used to arrive at correlations between various properties of alloys and their compositions and manufacturing process parameters. Data-driven approaches are of significant interest to materials engineers especially in arriving at extreme value properties such as cyclic fatigue, where the current state-of-the-art physics based models have severe limitations. Unfortunately, there is limited amount of documented success in these efforts. In this paper, we explore the application of different data science techniques, including feature selection and predictive modeling, to the fatigue properties of steels, utilizing the data from the National Institute for Material Science (NIMS) public domain database, and present a systematic end-to-end framework for exploring materials informatics. Results demonstrate that several advanced data analytics techniques such as neural networks, decision trees, and multivariate polynomial regression can achieve significant improvement in the prediction accuracy over previous efforts, with R2 values over 0.97. The results have successfully demonstrated the utility of such data mining tools for ranking the composition and process parameters in the order of their potential for predicting fatigue strength of steels, and actually develop predictive models for the same.

Original languageEnglish (US)
Pages (from-to)90-108
Number of pages19
JournalIntegrating Materials and Manufacturing Innovation
Volume3
Issue number1
DOIs
StatePublished - Dec 1 2014

Keywords

  • Data mining
  • Materials informatics
  • Processing-property linkages
  • Regression analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Industrial and Manufacturing Engineering

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