Prosthetic finger for fingertip tactile sensing via flexible chromatic optical waveguides.

Building prosthetics indistinguishable from human limbs to accurately receive and transmit sensory information to users not only promises to radically improve the lives of amputees, but also shows potential in a range of robotic applications. Currently, a mainstream approach is to embed electrical or optical sensors with force/thermal sensing functions on the surface or inside of prosthetic fingers. Compared with electrical sensing technologies, tactile sensors based on stretchable optical waveguides have the advantages of easy fabrication, chemical safety, environmental stability, and compatibility with prosthetic structural materials. However, so far, research has mainly focused on the perception of finger joint motion or external press, and there is still a lack of study on optical sensors with fingertip tactile capabilities (such as texture, hardness, slip detection, etc.). Here we report a 3D printing prosthetic finger with flexible chromatic optical waveguides implanted at the fingertip. The finger achieves distributed displacement/force sensing detection, and exhibits high sensitivity, fast response and good stability. The finger can be used to conduct active sensory experiments, and the detection parameters include object contour, hardness, slip direction and speed, temperature, etc. Finally, exploratory research on identifying and manipulating objects is carried out with this finger. The developed prosthetic finger can artificially recreate touch perception and realize complex functions such as note-writing analysis and braille recognition.

A New Strategy for Disc Cutter Wear Status Perception Using Vibration Detection and Machine Learning.

Carrying out status monitoring and fault-diagnosis research on cutter-wear status is of great significance for real-time understanding of the health status of Tunnel Boring Machine (TBM) equipment and reducing downtime losses. In this work, we proposed a new method to diagnose the abnormal wear state of the disc cutter by using brain-like artificial intelligence to process and analyze the vibration signal in the dynamic contact between the disc cutter and the rock. This method is mainly aimed at realizing the diagnosis and identification of the abnormal wear state of the cutter, and is not aimed at the accurate measurement of the wear amount. The author believes that when the TBM is operating at full power, the cutting forces are very high and the rock is successively broken, resulting in a complex circumstance, which is inconvenient to vibration signal acquisition and transmission. If only a small thrust is applied, to make the cutters just contact with the rock (less penetration), then the cutters will run more smoothly and suffer less environmental interference, which would be beneficial to apply the method proposed in this paper to detect the state of the cutters. A specific example was to use the frequency-domain characteristics of the periodic vibration waveform during the contact between the cutter and the granite to identify the wear status (including normal wear state, wear failure state, angled wear failure state) of the disc cutter through the artificial neural network, and the diagnosis accuracy rate is 90%.