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

Ankit Agrawal*, Alok Choudhary

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Fatigue strength is one of the most important mechanical properties of steel. Here we describe the development and deployment of data-driven ensemble predictive models for fatigue strength of a given steel alloy represented by its composition and processing information. The forward models for PSPP relationships (predicting property of a material given its composition and processing parameters) are built using over 400 experimental observations from the Japan National Institute of Materials Science (NIMS) steel fatigue dataset. Forty modeling techniques, including ensemble modeling were explored to identify the set of best performing models for different attribute sets. Data-driven feature selection techniques were also used to find a small non-redundant subset of attributes, and the processing/composition parameters most influential to fatigue strength were identified to inform future design efforts. The developed predictive models are deployed in a user-friendly online web-tool available at http://info.eecs.northwestern.edu/SteelFatigueStrengthPredictor.

Original languageEnglish (US)
Pages (from-to)389-400
Number of pages12
JournalInternational Journal of Fatigue
Volume113
DOIs
StatePublished - Aug 2018

Keywords

  • Ensemble learning
  • Fatigue strength
  • Materials informatics
  • Online tool
  • Supervised learning

ASJC Scopus subject areas

  • Modeling and Simulation
  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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