Touch: A New Paradigm Based on Machine Tactile Sensation for Surface Microdefect Detection

Surface microdefect detection is a major challenge in advanced manufacturing, while the existing computer vision-based methods struggle to detect micrometer-scale defects, which are invisible to the naked eye. To overcome this problem, this article develops a novel system based on machine-tactile-sensation (MTS) to detect the microdefect. The MTS-based inspection system uses vision-based tactile sensor to convert the tactile signals into visual image, which can capture the fine geometric morphology of the microdefects. Furthermore, considering the limited resolution of the transformed signal, TouchNet, which integrates a label-guided diversity contrastive learning method with an adaptive receptive field selection module, is introduced for defect recognition, which can leverage prior knowledge to guide hyperspherical clustering and employs Gram regularization to prevent feature degradation. The experimental results on the HUST-Tactile dataset indicate that the proposed method can detect the microdefects as small as 0.01 mm with 97.06% accuracy. It also outperforms the state-of-the-art models by 1.33% and 8.46% on the public NEU-CLS and RSW-C datasets, respectively.