7. Contribution of Machine Learning to the characterization of wear debris and the prediction of tribological phenomena

Recent developments in machine learning (ML) techniques are opening up new perspectives for the analysis and interpretation of tribological phenomena, particularly in the context of wear debris characterization. Given the complexity and multidimensional nature of the data generated by characterization methods (electron microscopy, spectroscopy, online detection, etc.), ML algorithms are particularly effective at extracting complex, often nonlinear correlations that elude traditional statistical approaches (Figure 15 ).