Analysis and Interpretation of Steering Geometry of Automobile Using Artificial Neural Network Simulation

Vehicle dynamics is the one of the most important factors in the analysis and predicting the steering behavior of automobile. The paper details the evaluation of the Artificial Neural Network (ANN) structures to estimate the steering geometry parameters of four wheel vehicle. One of the aspects of vehicle performance is performance of steering geometry. Steering geometry parameters kingpin inclination angle, caster angle, camber angle, toe angle, scrub radius, toe in and toe out are measured using alignment techniques and caster/camber gauges. Suspension system components pivot upon a rubber bushing which is compressed between an inner and outer metal sleeve. Excess clearance developed in the joints of suspension system in turn causes changes in steering geometry. This is obviously essential for any automobile for a major challenge in terms of operation, performance, servicing and maintenance. ANN models applicable to each of these steering parameters were developed. Steering geometry is evaluated through the independent and dependent variables of front suspension. Dependent variables such as steering geometry parameters kingpin inclination angle, caster angle, camber angle, toe angle, scrub radius, toe in and toe out are determined with the help of independent variables. These dependent variables are validated through ANN simulation. The result obtained through ANN is in close agreement to the experimental observation.


Introduction
The present methods of observing the steering parameters are not suitable and Network (ANN) is generally the software systems that imitate the neural network of the human brain [3].The complex relationship between the input and output is identifying by the powerful tool of neural networks.The study indicates that the expert systems such as ANN are efficient in simulating the complicated phenomena due to its non-linear structures [4].

Procedure for Formulation of ANN Model
The experimental data based modeling has been achieved based on experimental data for the seven dependent pi terms.In such complex phenomenon involving non-linear kinematics where in the validation of experimental data based models Table 1.Values computed by experimental observation and ANN simulation. Sr.N

Values calculated as per experimental observations
Values calculated as per ANN simulation MATLAB software is selected for developing ANN simulation.The following steps are involved for developing the ANN algorithm is as under.
• The experimental data is separated into two parts viz.input data and the output data pi terms.The input data and output data are imported to the program respectively.• The prestd function is used to read the input and output data and appropriately sized.
• The input and output data is normalized in preprocessing step using mean and standard deviation.
• The input and output data is then categorized in three categories viz.testing, validation and training.From the 18 observations, initial 75% of the observations is selected for training, last 75% data for validation and middle overlapping 50% data for testing.
• The data is then stored in structures for training, testing and validation.
• The feed forward back propagation is selected based on the data.
• Using the training data the network is then trained.The actual data and target data are compared and simulate the network.
The regression analysis and the representation are done through the standard functions.The values of regression coefficient and the equation of regression lines are represented on the seven different graphs plotted for the seven dependent pi terms [7] [8].The detailed ANN program used for evaluation the steering geometry is provided in the Appendix.program shown in Appendix is run on the MATLAB software.The ANN Outputs consists of all the steering parameters are shown in the Table 1.

Conclusion
An ANN model has been developed for predicating steering behavior How to cite this paper: Belkhode, P.N. (2019) Analysis and Interpretation of Steering Geometry of Automobile Using Artifi-P.N. Belkhode DOI: 10.4236/eng.2019.114016232 Engineering have limitation in the measurements and predicting the behavior of front suspension of an automobile [1] [2].Steering geometry parameters kingpin inclination angle, caster angle, camber angle, toe angle, scrub radius, toe in and toe out are measured using alignment techniques and caster/camber gauges.Steering parameters change from place to place.The analysis of parameters requires a mathematical model which can usefully to the observed variation and which then provides a basis for generalization, prediction and interpretation.Steering behavior is predicated by the by the experimental investigation.Artificial Neural

Figure 1
Figure 1 shows the structure and basic elements for designing artificial neural network.The capability of the ANN model is to generalize unseen data dependent on several factors.These factors are appropriate selection of input output parameters, the distribution of the input and output dataset and the format of the presentation of the dataset to the neural network as shown in Figure 2. The output parameters of the model is then Kingpin angle, Camber angle, Caster angle, Toe angle, Toe in, Toe out, Scrub radius.Details of the input and output parameters of the proposed ANN model are illustrated.MATLAB is used for training the network architecture.Figure3shows the optimal network archi-

Figure 3
shows the optimal network architecture is formed by training.The network is trained with the help experimental results shown in the Table 1.The experimental results are imported to the trained network of ANN program as shown in the Figure 3 ANN Topology.ANN P. N. Belkhode DOI: 10.4236/eng.2019.114016235 Engineering
. The model was proved to be successful in terms of agreement with actual values for experimentation.The feasibility and rationality of the ANN Model of the testing data which includes all the steering geometry Kingpin angle, Camber angle, Caster angle, Toe angle, Toe in, Toe out, Scrub radius is proved to be in close agreement.
Simulation of the observed data.Simulation consists of three layers.First layer is known as input layer.The input neurons in input layer are equal to the number of independent variables.Second layer is known as hidden layer.It consists of seven numbers of neurons.The third layer is output layer.It contains one neuron as one of dependent variables at a time.For the ANN multilayer feed forward topology is decided.
Table 1 shows the comparison of the steering geometry parameters evaluated through the experimental observation and ANN Program.It can be concluded that ANN model performs accurately to determine the optimal values.