摘要
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%26apos;s actual friction coefficient.
- 出版日期2012-8