The sub-Gramian method as a tool for the automotive suspension tuning

2018 ◽  
Author(s):  
Dmitry E. Kataev ◽  
Evgeniy Y. Kutyakov ◽  
Yuri Vershinin
Keyword(s):  
Automotive Suspension ◽  
Suspension Tuning

Simulation and Analysis of Two-Mass Suspension Modification Using MATLAB Programming

2019 ◽  
Vol 3 (1) ◽  
pp. 160-165
Author(s):  
Hendry D. Chahyadi
Keyword(s):  
Vibrational Energy ◽  
State Space Model ◽  
Mass System ◽  
Modeling And Analysis ◽  
The Road ◽  
Final Project ◽  
Quarter Car ◽  
Automotive Suspension ◽  
Mass Spring ◽  
Matlab Programming

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.

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

2021 ◽  
pp. 095440702110241
Author(s):  
Byunghyun Kang ◽  
Cheol Choi ◽  
Daeun Sung ◽  
Seongho Yoon ◽  
Byoung-Ho Choi
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Frictional Force ◽  
Prediction Models ◽  
Statistical Tests ◽  
Network Models ◽  
Multiple Logistic Regression ◽  
Neural Network Models ◽  
Automotive Suspension

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.

On the Application of the Lu-Gre Model to Simulate Joint Friction in Multi-Body Systems

2011 ◽  
Author(s):  
Kedar Gajanan Kale ◽  
Rajiv Rampalli
Keyword(s):  
Friction Model ◽  
Lugre Model ◽  
Friction Forces ◽  
Joint Friction ◽  
Modeling Techniques ◽  
Static Regime ◽  
Increasing Demand ◽  
Automotive Suspension ◽  
Multi Body

Advances in the application of multi-body simulation technology to real world problems have led to an ever increasing demand for higher fidelity modeling techniques. Of these, accurate modeling of friction is of strategic interest in applications such as control system design, automotive suspension analysis, robotics etc. Joints (sometimes referred to as constraints) play an important role in defining the dynamics of a multi-body system. Hence, it is imperative to accurately account for friction forces arising at these joints due to the underlying interface dynamics. In this paper, we discuss the application of LuGre, a dynamic friction model to simulate joint friction. We choose the LuGre model due to its ability to capture important effects such as the Stribeck effect and the Dahl effect. The native 1-d LuGre model is extended to formulate friction computations for non-trivial joint geometries and dynamics in 2 and 3 dimensions. It is also extended to work in the quasi-static regime. Specific applications to revolute, cylindrical and spherical joints in multi-body systems are discussed. Finally, an engineering case study on the effects of joint friction in automotive suspension analysis is presented.

Direct Measurement of the Dynamic Side Forces on the Automotive Suspension Strut

2007 ◽  
Author(s):  
Seung-Hoon Choi ◽  
Man-Joon Lee ◽  
Je-Soo Park ◽  
Sung-Soo Park ◽  
Jae-Keun Park
Keyword(s):  
Direct Measurement ◽  
Automotive Suspension ◽  
Suspension Strut

Optimization Design of Automobile Suspension Springs Based on BP

2010 ◽  
Vol 42 ◽  
pp. 82-85 ◽  
Author(s):  
Xiao Hui Shi ◽  
Hong Li Gao ◽  
Ming Heng Xu
Keyword(s):  
Optimization Design ◽  
Maximum Stress ◽  
Stress Model ◽  
Analysis Method ◽  
Parallel Genetic Algorithm ◽  
Distribution Law ◽  
Suspension Spring ◽  
Automobile Suspension ◽  
Stress Point ◽  
Automotive Suspension

In order to optimize the arithmetic of the automobile suspension springs, the optimization algorithm of spring stress model based on BP neural networks was proposed. The various models of suspension spring were aggregated and analyzed, the parallel genetic algorithm for the suspension springs was proposed in this paper as well. And the spring models can be optimized fast and efficiently by the algorithm. Aiming at different models, the maximum stress point and the distribution law are given by means of experimental verifications. A new stress analysis method and a new estimate rule for the design of automotive suspension springs are provided by this method.

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