Xiaopeng Yue
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiaopeng Yue has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Jinbo Xiang
College of Information Engineering, Hangzhou Dianzi University, Hangzhou 311305, China
1 shared publication - Jing Ni
School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
1 shared publication - Junqiang Zheng
School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310005, China
1 shared publication - Kai Feng
School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
1 shared publication - Lihong Li
College of Information Engineering, Hangzhou Dianzi University, Hangzhou 311305, China
1 shared publication - Rulan Dai
School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310005, China
1 shared publication
Latest Publications
- Open Access
Research on Output Performance of Linear Motor Reciprocating Pump Based on Mechanical-Hydraulic-Load Coupling Model
To study the output performance of the multi-unit linear motor reciprocating pump group, a mechanical-hydraulic-load coupling model was established based on the electromagnetic output model of the linear oscillating motor and the load model of the hydraulic pump system. The dynamic characteristics of the multi-unit linear motor reciprocating pump and the output performance under different input signal parameters were studied using the AMEsim-Simulink co-simulation method, and the validity of the simulation model was verified by the prototype output performance experiment. The analysis result indicates that the parameters of the input signal have a greater impact on the performance of the linear motor reciprocating pump. And the sine wave signal is more suitable as the control signal of the linear motor reciprocating pump, while the staggered parallel mode can greatly reduce the flow output pulsation rate of the even number pump group.
Open AccessResearch on Output Performance of Linear Motor Reciprocating Pump Based on Mechanical-Hydraulic-Load Coupling Model
To study the output performance of the multi-unit linear motor reciprocating pump group, a mechanical-hydraulic-load coupling model was established based on the electromagnetic output model of the linear oscillating motor and the load model of the hydraulic pump system. The dynamic characteristics of the multi-unit linear motor reciprocating pump and the output performance under different input signal parameters were studied using the AMEsim-Simulink co-simulation method, and the validity of the simulation model was verified by the prototype output performance experiment. The analysis result indicates that the parameters of the input signal have a greater impact on the performance of the linear motor reciprocating pump. And the sine wave signal is more suitable as the control signal of the linear motor reciprocating pump, while the staggered parallel mode can greatly reduce the flow output pulsation rate of the even number pump group.
- Open Access
Contribution Ratio Assessment of Process Parameters on Robotic Milling Performance
Robotic milling has broad application prospects in many processing fields. However, the milling performance of a robot in a certain posture, such as in face milling or grooving tasks, is extremely sensitive to process parameters due to the influence of the serial structure of the robot system. Improper process parameters are prone to produce machining defects such as low surface quality. These deficiencies substantially decrease the further application development of robotic milling. Therefore, this paper selected a certain posture and carried out the robotic flat-end milling experiments on a 7075-T651 high-strength aeronautical aluminum alloy under dry conditions. Milling load, surface quality and vibration were selected to assess the influence of process parameters like milling depth, spindle speed and feed rate on the milling performance. Most notably, the contribution ratio based on the analysis of variance (ANOVA) was introduced to statistically investigate the relation between parameters and milling performance. The obtained results show that milling depth is highly significant in milling load, which had a contribution ratio of 69.25%. Milling depth is also highly significant in vibration, which had a contribution ratio of 51.41% in the X direction, 41.42% in the Y direction and 75.97% in the Z direction. Moreover, the spindle speed is highly significant in surface roughness, which had a contribution ratio of 48.02%. This present study aims to quantitatively evaluate the influence of key process parameters on robotic milling performance, which helps to select reasonable milling parameters and improve the milling performance of the robot system. It is beneficial to give full play to the advantages of robots and present more possibilities of robot applications in machining and manufacturing.
Open AccessContribution Ratio Assessment of Process Parameters on Robotic Milling Performance
Robotic milling has broad application prospects in many processing fields. However, the milling performance of a robot in a certain posture, such as in face milling or grooving tasks, is extremely sensitive to process parameters due to the influence of the serial structure of the robot system. Improper process parameters are prone to produce machining defects such as low surface quality. These deficiencies substantially decrease the further application development of robotic milling. Therefore, this paper selected a certain posture and carried out the robotic flat-end milling experiments on a 7075-T651 high-strength aeronautical aluminum alloy under dry conditions. Milling load, surface quality and vibration were selected to assess the influence of process parameters like milling depth, spindle speed and feed rate on the milling performance. Most notably, the contribution ratio based on the analysis of variance (ANOVA) was introduced to statistically investigate the relation between parameters and milling performance. The obtained results show that milling depth is highly significant in milling load, which had a contribution ratio of 69.25%. Milling depth is also highly significant in vibration, which had a contribution ratio of 51.41% in the X direction, 41.42% in the Y direction and 75.97% in the Z direction. Moreover, the spindle speed is highly significant in surface roughness, which had a contribution ratio of 48.02%. This present study aims to quantitatively evaluate the influence of key process parameters on robotic milling performance, which helps to select reasonable milling parameters and improve the milling performance of the robot system. It is beneficial to give full play to the advantages of robots and present more possibilities of robot applications in machining and manufacturing.