Data-Driven Model for Estimation of Friction Coefficient Via Informatics Methods
- First Online:
- 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
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.