Analysis and Research on Thermal-Force Coupling Performance of a Vehicle Controller PCB Board

Author(s):  
Yajuan Chen
2016 ◽  
Vol 693 ◽  
pp. 243-250
Author(s):  
Zhi Zhong Guo ◽  
Yun Shun Zhang ◽  
Shi Hao Liu

It is discovered that the vibration resistance of spindle systems needs to be improved based on the statics analysis, modal analysis and heating-force coupling analysis of spindle systems of CNC gantry machine tools. The design variables of optimization are set according to sensitivity analysis, multi-objective and dynamic optimization design is realized and its designing scheme is gained for spindle structure. The research results show that vibration resistance can be improved without change of the quality and static property of spindle systems of CNC gantry machine tools.


1994 ◽  
Vol 34 (2) ◽  
pp. 276-282 ◽  
Author(s):  
M Saigusa ◽  
S Moriyama ◽  
T Fujii ◽  
H Kimura ◽  
M Sato ◽  
...  
Keyword(s):  

2021 ◽  
pp. 1-9
Author(s):  
Yibin Deng ◽  
Xiaogang Yang ◽  
Shidong Fan ◽  
Hao Jin ◽  
Tao Su ◽  
...  

Because of the long propulsion shafting of special ships, the number of bearings is large and the number of measured bearing reaction data is small, which makes the installation of shafting difficult. To apply a small amount of measured data to the process of ship installation so as to accurately calculate the displacement value in the actual installation, this article proposes a method to calculate the displacement value of shafting intermediate bearing based on different confidence-level training samples. Taking a ro-ro ship as the research object, this research simulates the actual installation process, gives a higher confidence level to a small amount of measured data, constructs a new training sample set for machine learning, and finally obtains the genetic algorithm-backpropagation(GABP) neural network reflecting the actual installation process. At the same time, this research compares the accuracy between different confidence-level training sample shafting neural network and the shafting neural network without measured data, and the results show that the accuracy of shafting neural network with different confidence-level training samples is higher. Although as the adjustment times and the number of measured data increase, the network accuracy is significantly improved. After adding four measured data, the maximum error is within 1%, which can play a guiding role in the ship propulsion shafting alignment. Introduction With the rapid development of science and technology in the world, special ships such as engineering ships, official ships, and warships play an important role (Carrasco et al. 2020; Prill et al. 2020). Some ships of this special type are limited by various factors such as the stern line of engine room, hull stability, and operation requirements. They usually adopt the layout of middle or front engine room, which causes the propulsion system to have a longer shaft and the number of intermediate shafts and intermediate bearings exceeds two. This forms a so-called multisupport shafting (Lee et al. 2019) and it increases the difficulty of shafting alignment because of the force-coupling between the bearings (Lai et al. 2018a, 2018b). The process of the existing methods for calculating the displacement value is complex, and because of the influence of installation error and other factors, it is necessary to adjust the bearing height several times to make the bearing reaction meet the specification requirements(Kim et al. 2017, Ko et al. 2017). So how to predict the accurate displacement value of each intermediate bearing is the key to solving the problem of multisupport shafting intermediate bearing installation and calibration (Zhou et al. 2005, Xiao-fei et al. 2017).


Author(s):  
J. H. Choi ◽  
A. A. Shabana ◽  
Roger A. Wehage

Abstract In this investigation, a procedure is presented for the numerical solution of tracked vehicle dynamics equations of motion. Tracked vehicles can be represented as two kinematically decoupled subsystems. The first is the chassis subsystem which consists of chassis, rollers, idlers, and sprockets. The second is the track subsystem which consists of track links interconnected by revolute joints. While there is dynamic force coupling between these two subsystems, there is no inertia coupling since the kinematic equations of the two subsystems are not coupled. The objective of the procedure developed in this investigation is to take advantage of the fact that in many applications, the shape of the track does not significantly change even though the track links undergo significant configurations changes. In such cases the nonlinearities propagate along the diagonals of a velocity influence coefficient matrix. This matrix is the only source of nonlinearities in the generalized inertia matrix. A permutation matrix is introduced to minimize the number of generalized inertia matrix LU factor evaluations for the track.


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