Predicting the adhesion strength of micropatterned surfaces using supervised machine learning

Abstract

Fibrillar dry adhesives have shown great potential in many applications thanks to their tunable adhesion, notably for pick-and-place handling of fragile objects. However, controlling and monitoring alignment with the target objects is mandatory to enable reliable handling. In this paper, we present an in-line monitoring system that allows optical analysis of an array of individual fibrils (with a contact radius of 350 µm) in contact with a smooth glass substrate, followed by the prediction of their adhesion performance. Images recorded at maximum compressive preload represent characteristic contact signatures that were used to extract visual features. These features, in turn, were used to create a linear model and to train different linear and non-linear regression models for predicting adhesion force depending on the misalignment angle. Support vector regression and boosted tree models exhibited highest accuracies and outperformed an analytical model reported in literature. Overall, this new approach enables predictions in gripping objects by contact observations in near real-time, which likely improves the reliability of handling operations.