The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sens...The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.展开更多
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 ...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.Numerical modeling of the cutting force is performed to capture the relationship between the cutting force and the depth of cut in the bone milling process.The force model is used to calibrate the training data to improve the recognition accuracy.Wavelet package transform is used for signal processing to understand bone-cutting phenomena using sound signals.The proposed system succeeds to monitor the bone milling process to reduce the surgical risk.Experiments on standard bone specimens and vertebrae also indicate that the proposed approach has considerable potential for use in computer-assisted and robot-assisted bone-cutting systems used in various types of surgery.展开更多
The pressure signal in the lifting cylinder of the shearer is selected as feature signal, its mean-square deviation is extracted as the feature variable in this paper. The authors put forward a new method of recognizi...The pressure signal in the lifting cylinder of the shearer is selected as feature signal, its mean-square deviation is extracted as the feature variable in this paper. The authors put forward a new method of recognizing the shearer’s cutting state based on pattern recognition. According to this, the completed controI software produced a satisfactory experiment result on the artificial longwall face in the laboratory, Finally the authors look forward to the prospect of the introduction of the artificial neural network theory into this field.展开更多
文摘The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.
基金the Open Research Fund of State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology(Grant No.DMETKF2020004).
文摘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.Numerical modeling of the cutting force is performed to capture the relationship between the cutting force and the depth of cut in the bone milling process.The force model is used to calibrate the training data to improve the recognition accuracy.Wavelet package transform is used for signal processing to understand bone-cutting phenomena using sound signals.The proposed system succeeds to monitor the bone milling process to reduce the surgical risk.Experiments on standard bone specimens and vertebrae also indicate that the proposed approach has considerable potential for use in computer-assisted and robot-assisted bone-cutting systems used in various types of surgery.
文摘The pressure signal in the lifting cylinder of the shearer is selected as feature signal, its mean-square deviation is extracted as the feature variable in this paper. The authors put forward a new method of recognizing the shearer’s cutting state based on pattern recognition. According to this, the completed controI software produced a satisfactory experiment result on the artificial longwall face in the laboratory, Finally the authors look forward to the prospect of the introduction of the artificial neural network theory into this field.
