scholarly journals Joint simulation machining study of inducer based on MAX-PAC and VERICUT

2021 ◽  
Vol 1885 (2) ◽  
pp. 022049
Author(s):  
Zixuan Li ◽  
Yun He ◽  
Zheng Cao ◽  
Xuelin Lei ◽  
Hang Zhang ◽  
...  
Keyword(s):  
Joint Simulation

Simulation of Valve Position Feedback Mechanism Based on Pro/E and ADAMS

2014 ◽  
Vol 721 ◽  
pp. 244-248
Author(s):  
You Jun Fan ◽  
Fei Li ◽  
Hua Tian Zhao
Keyword(s):  
Feedback Mechanism ◽  
Dynamics Model ◽  
Joint Simulation ◽  
Force And Motion ◽  
Motion State ◽  
Position Feedback ◽  
Displacement Angle ◽  
Valve Position ◽  
Relationship Of ◽  
The Relationship

In traditional valve position feedback mechanism design, it is tested repeatedly and improvement after processing prototype, the process is complex and workload. Using Pro/E and ADAMS, the overall mechanical structure of the valve position feedback mechanism for joint simulation, and an analysis of the kinematics and dynamics model, simplified the design process of the repeated calculation, get the relationship of stem displacement-angle between gear, gear meshing force and motion state of the stem, the simulation value compared with the theoretical value, tallies with the data and shows that the simulation is reasonable.

scholarly journals Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yangyang Shi ◽  
Qiongqiong Li ◽  
Shengqiang Bu ◽  
Jiafu Yang ◽  
Linfeng Zhu
Keyword(s):  
Convergence Speed ◽  
Random Tree ◽  
Tree Search ◽  
Vehicle Model ◽  
Turning Angle ◽  
Tree Algorithm ◽  
Slow Convergence ◽  
Joint Simulation ◽  
Simulation Based ◽  
Vehicle Path

Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.

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