Correlation of Microhardness with Abrasive Wear of Thin Films: A Machine Learning Approach

Thin films find extensive applications in various industries, from electronics to aerospace, due to their unique properties and versatility. Understanding and predicting the mechanical behavior of these films, particularly their abrasive wear resistance, is crucial for optimizing their performance and longevity. This study explores the application of machine learning techniques to establish a correlation between microhardness, a fundamental mechanical property, and abrasive wear in thin films.Random Forest Regressor model was used to predict abrasive wear based on microhardness. The model’s performance was evaluated using Mean Squared Error and Root Mean Squared Error metrics.The results illustrated the feasibility of using machine learning to correlate microhardness with abrasive wear of thin films. Additionally, visualizations are presented that depict the predicted values against actual measurements, providing a clear representation of the model’s performance.