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

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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 R 2 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.

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Acknowledgments

The authors EWB, KRM, WGS, SRP, and SBS gratefully acknowledge the support of the Office of Naval Research under grant number N000141010165. CSK and KR acknowledge the support from the NSF-ARI Program under grant number CMMI 09-389018 and the Army Research Office under grant number W911NF-10-0397. KR acknowledges the support from the Wilkinson Professorship of Interdisciplinary Engineering. The authors also thank Jonathan Liddy, former undergraduate student from the University of Florida, for his role in the compilation of the properties from the literature used for the material dataset in this study.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, University of Florida, Gainesville, FL, 32611, USA

    Eric W. Bucholz, Simon R. Phillpot & Susan B. Sinnott

  2. Department of Materials Science and Engineering, Iowa State University, Ames, IA, 50011, USA

    Chang Sun Kong & Krishna Rajan

  3. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, 32611, USA

    Kellon R. Marchman & W. Gregory Sawyer

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  1. Eric W. Bucholz
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  2. Chang Sun Kong
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  3. Kellon R. Marchman
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  4. W. Gregory Sawyer
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  5. Simon R. Phillpot
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  6. Susan B. Sinnott
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  7. Krishna Rajan
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Corresponding authors

Correspondence to Susan B. Sinnott or Krishna Rajan.

Appendix

Appendix

See Table 2.

Table 2 Material dataset with 16 properties and 38 materials used to develop predictive model for friction coefficient
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Bucholz, E.W., Kong, C.S., Marchman, K.R. et al. Data-Driven Model for Estimation of Friction Coefficient Via Informatics Methods. Tribol Lett 47, 211–221 (2012). https://doi.org/10.1007/s11249-012-9975-y

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