Zhenzhi Ying 교수

Abstract Efficient monitoring of bone milling conditions in orthopedic and neurosurgical surgery can prevent tissue, bone, and tool damage, and reduce surgery time. Current researches are mainly focused on recognizing the cutting state using force signal. However, the force signal during the milling process is difficult and expensive to acquire. In this study, a neural network-based method is proposed to recognize the cutting state and force during the bone milling process using sound signals. N

In spinal surgery, the surgeon needs superb skills to determine the extent of penetration of cutting tool for preventing the damage of nerves or organs. Thus, a cutting system with autonomous penetration detection would drastically improve the safety and efficiency of surgery. In this study, a hand-hold bone cutting system for laminectomy with on-line autonomous penetration perception of bone is presented. Since the penetration during operation process is invisible, a practical surgeon uses forc

Data-driven prediction of machine tool downtime is key to improving the availability and effective life span of machine tools. However, existing methods lack the incorporation of domain-specific knowledge into recognition algorithms for feature extraction. To utilize this content-rich information flow from embedded sensors, a hybrid model prediction method based on deep learning is proposed herein. A deep residual network with wavelet packet transform is constructed to predict the remaining tool

The vague interpretation of myoelectrical signals on the residual limb end makes restoring dexterous hand function in amputees still impossible. Understanding motor control between human motion intention and synaptic inputs to motor neurons also remains a significant challenge. The neural decoding methods of surface EMG signals remains challenging, which limit the application of robot hand in real life. Herein, we propose and substantiate a human-machine interface for motor control that introduc

Titanium alloys are widely used in aerospace applications due to their excellent material properties. Nevertheless the low machining speed and short tool life represent the high cost of machining titanium alloys. Laser assisted machining (LAM) has been developed to be an advanced technique to machine this kind of hard-to-machine material and to improve machinability. Most researches in this field are conducted to assess the effect of various cutting parameters to optimize cutting condition and m