A high recognition accuracy tactile sensor based on boron nitride nanosheets/epoxy composites for material identification

. Combining deep machine learning and the friction electric effect, we developed a material recognition system for TENG sensors with integrated fatigue testing, data processing, and display modules. Following the training of the convolutional neural network (CNN) model with friction electrical signals generated by TENGs, the model demonstrated high accuracy in recognizing eight different materials, with a confusion matrix accuracy of 100%. Then, a sensor was developed for real-time device monitoring, with recognition accuracy of 100%, 100%, 55% and 49% for four kinds of materials. This work will further facilitate the development of a material perception system in the machine intelligence field.