An automotive suspension strut using compressible magnetorheological fluids

2005 ◽  
Author(s):  
Sung-Ryong Hong ◽  
Gang Wang ◽  
Wei Hu ◽  
Norman M. Wereley ◽  
Jack Niemczuk
2007 ◽  
Author(s):  
Seung-Hoon Choi ◽  
Man-Joon Lee ◽  
Je-Soo Park ◽  
Sung-Soo Park ◽  
Jae-Keun Park

2019 ◽  
Vol 3 (1) ◽  
pp. 160-165
Author(s):  
Hendry D. Chahyadi

The designs of automotive suspension system are aiming to avoid vibration generated by road condition interference to the driver. This final project is about a quarter car modeling with simulation modeling and analysis of Two-Mass modeling. Both existing and new modeling are being compared with additional spring in the sprung mass system. MATLAB program is developed to analyze using a state space model. The program developed here can be used for analyzing models of cars and vehicles with 2DOF. The quarter car modelling is basically a mass spring damping system with the car serving as the mass, the suspension coil as the spring, and the shock absorber as the damper. The existing modeling is well-known model for simulating vehicle suspension performance. The spring performs the role of supporting the static weight of the vehicle while the damper helps in dissipating the vibrational energy and limiting the input from the road that is transmitted to the vehicle. The performance of modified modelling by adding extra spring in the sprung mass system provides more comfort to the driver. Later on this project there will be comparison graphic which the output is resulting on the higher level of damping system efficiency that leads to the riding quality.


Author(s):  
Jingzhen Cheng ◽  
Kuo Liu ◽  
Zuocai Zhang ◽  
Zhouqiao Wei ◽  
Yinghui Ma ◽  
...  

Author(s):  
Byunghyun Kang ◽  
Cheol Choi ◽  
Daeun Sung ◽  
Seongho Yoon ◽  
Byoung-Ho Choi

In this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.


Sign in / Sign up

Export Citation Format

Share Document