[HTML][HTML] 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 …
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 …
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