Tribology Letters

, Volume 47, Issue 2, pp 211–221

Data-Driven Model for Estimation of Friction Coefficient Via Informatics Methods

  • Eric W. Bucholz
  • Chang Sun Kong
  • Kellon R. Marchman
  • W. Gregory Sawyer
  • Simon R. Phillpot
  • Susan B. Sinnott
  • Krishna Rajan
Original Paper

DOI: 10.1007/s11249-012-9975-y

Cite this article as:
Bucholz, E.W., Kong, C.S., Marchman, K.R. et al. Tribol Lett (2012) 47: 211. doi:10.1007/s11249-012-9975-y

Abstract

As technologies progress, the development of new mechanical systems demands the rapid determination of friction coefficients of materials. Data mining and materials informatics methods are used here to generate a predictive model that enables efficient high-throughput screening of ceramic materials, some of which are candidate high-temperature, solid-state lubricants. Through the combination of principal component analysis and recursive partitioning using a small dataset comprised of intrinsic material properties, we develop a decision tree-based model comprised of if-then rules which estimates the friction coefficients of a wide range of materials. This data-driven model has a high degree of accuracy with an R2 value of 0.8904 and provides a range of possible friction coefficients that accounts for the possible variability of a material’s actual friction coefficient.

Keywords

CeramicsStatistical analysisTribology databasesUnlubricated friction

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Eric W. Bucholz
    • 1
  • Chang Sun Kong
    • 2
  • Kellon R. Marchman
    • 3
  • W. Gregory Sawyer
    • 3
  • Simon R. Phillpot
    • 1
  • Susan B. Sinnott
    • 1
  • Krishna Rajan
    • 2
  1. 1.Department of Materials Science and EngineeringUniversity of FloridaGainesvilleUSA
  2. 2.Department of Materials Science and EngineeringIowa State UniversityAmesUSA
  3. 3.Department of Mechanical and Aerospace EngineeringUniversity of FloridaGainesvilleUSA