Adhesion Testing System Based on Convolutional Neural Networks for Quality Inspection of Flexible Strain Sensors

Manufacturing reliable strain sensors based on nanostructured materials faces several challenges, such as ensuring quality inspection of the adhesion between the active sensor and its substrate. In order to overcome this, the use of a deep learning-based technique is proposed herein to determine the in-situ adhesion strength. This study conducts an adhesion strength analysis between carbon nanotubes (CNTs) over a polymeric substrate (as components of a strain sensor), using image analysis. In line with the edge-computing paradigm, a novel inspection system is presented. An embedded processor equipped with a convolutional neural network architecture is used to inspect the CNT adhesion deposited on a substrate surface using deep learning semantic segmentation. This determines the relative concentration of CNTs covering the peeled area and its spatial probabilistic distribution map. Experimental results demonstrate the quality inspection precision, both before and after peeling-tape tests with an accuracy of up to 96.56%.