Machine learning model to cluster and map tribocorrosion regimes in feature space
This work addresses tribocorrosion mapping for materials engineering, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of predicting tribosystem performance by developing a machine learning approach to generate tribocorrosion maps, using unsupervised clustering and SVM classification, and compared the results with standard literature maps.
Tribocorrosion maps serve the purpose of identifying operating conditions for acceptable rate of degradation. This paper proposes a machine learning based approach to generate tribocorrosion maps, which can be used to predict tribosystem performance. First, unsupervised machine learning is used to identify and label clusters from tribocorrosion experimental data. The identified clusters are then used to train a support vector classification model. The trained SVM is used to generate tribocorrosion maps. The generated maps are compared with the standard maps from literature.