Predicting adhesion strength of micropatterned surfaces using gradient boosting models and explainable artificial intelligence visualizations

Fibrillar dry adhesives are widely used due to their effectiveness in air and vacuum conditions. However, their performance depends on various factors. Previous studies have proposed analytical methods to predict adhesion strength on micro-patterned surfaces. However, the method lacks interpretation on which parameters are critical. This research utilizes gradient-boosting machine learning (ML) algorithms to accurately predict adhesion strength. Additionally, explainable machine learning (XML) methods are employed to interpret the underlying reasoning behind the predictions. The analysis demonstrates that gradient boosting models achieve a high correlation coefficient (R > 0.95) in accurately predicting pull-off force on micro-patterned surfaces. The use of XML methods provides insights into the importance of features, their interactions, and their contributions to specific predictions. This novel, explainable, and data-driven approach holds potential for real-time applications, aiding in the identification of critical features that govern the performance of fibrillar adhesives. Furthermore, it improves end-users' confidence by offering human-comprehensible explanations and facilitates understanding among non-technical audiences.