Yikai Shi

Experimental analysis of drilling process in cortical bone

Bone drilling is an essential part in orthopaedics, traumatology and bone biopsy. Prediction and control of drilling forces and torque are critical to the success of operations involving bone drilling. This paper studied the drilling force, torque and drilling process with automatic and manual drill penetrating into bovine cortical bone. The tests were performed on a drilling system which is used to drill and measure forces and torque during drilling. The effects of drilling speed, feed rate and drill bit diameter on force and torque were discussed separately. The experimental results were proven to be in accordance with the mathematic expressions introduced in this paper. The automatic drilling saved drilling time by 30-60% in the tested range and created less vibration, compared to manual drilling. The deviation between maximum and average force of the automatic drilling was 5N but 25N for manual drilling. To conclude, using the automatic method has significant advantages in control drilling force, torque and drilling process in bone drilling.

A method for optimal sizing hybrid energy storage system for smoothing Fluctuations of Wind Power

A hybrid energy storage system (HESS) is integrated with a wind farm to smooth out its fluctuations in order to reduce the impact on the grids. This paper presents a cutoff frequency method for optimizing the HESS for smoothing fluctuations of wind energy. The main goal of the proposed method is to find the rated power and capacity of HESS. Based on spectrum analysis results and power ramp rate, the best cutoff frequency, expected power and the minimum absorb power are determined. According to the characteristics of HESS and the safety specifications, the minimum sizing of the battery and the super capacitor were selected. The proposed method was verified based on historical wind power data and Chinese Standard of grid-connected.

Design, path planning improvement and test of a portable massage robot on human back

A massage robot that helps to improve the quality of human life has attracted more interests of researchers and consumers. A portable back massage robot that is compact and space-saving was designed to be used on human back instead of a traditional large-scale structure robot. To design the massage robot, the models of electric circuit, magnetic circuit and mechanics were analyzed to achieve optimal massage force. Parameters of the massage actuator are determined based on the influence analysis of the coil current, the coil turns and the distance between the moving core and the yoke on the electromagnetic force. The massage coverage of human back, which is used to calculate the massage effect, could be improved by an excellent path planning algorithm. This article proposed an efficient full covered path planning algorithm for the designed massage robot, and the relevant algorithm models were established. Simulation results show that the coil current is much more sensitive to electromagnetic force of the moving core compared to the other two factors, and the presented path planning algorithm completes full coverage of the massage robot on the back area. The experimental platform of the massage robot was built, and the influence of the input signal duty cycle, the input signal voltage and the hardness of the massage object on the massage effect was discussed by testing the values of acceleration. The tested results show that the massage effect is best when the duty cycle is in the range of 1/8–1/2. Meanwhile, the hardness of massage parts affects the massage intensity. The consistency between the tested results of path planning and simulation verifies the feasibility of the simulation procedure and indicates that the massage robot can attain the desired massage performance and realize the planned paths.

Optimal sizing and control of hybrid energy storage system for wind power using hybrid Parallel PSO-GA algorithm

This paper proposes a frequency-based method for sizing the hybrid energy storage system in order to smoothen wind power fluctuations. The main goal of the proposed method is to find the power and energy capacities of the hybrid energy storage system that minimizes the total cost per day of all the systems. The energy management strategy used in this paper is designed as a two-level energy distribution scheme: the first level is responsible for setting the output power of hybrid energy storage system, the second level manages the power flow between the battery and supercapacitor. The hybrid parallel particle swarm optimization-genetic algorithm (PSO-GA) optimization algorithm is proposed to solve the control parameters of energy management strategy. In addition, the proposed method uses the piecewise fitting function to describe the lifetime of battery. Obtained results show that the hybrid energy storage system with the proposed energy management strategy is able to offer the best performances for the wind power system in terms of cost and lifetime.