Analysis of an Automobile Suspension Arm Using the Robust Design Method

This thesis describes the analysis of lower automobile suspension arm using stochastic design improvement technique. The suspension system is one of the most important components of vehicle, which directly affects the safety, performance, noise level and style of it. The objectives of this study are to characterise the dynamic behavior, to investigate the influencing factors of lower suspension arm using FEM incorporating design of experiment (DOE) and artificial neural network (ANN) approach and to analysis the lower suspension arm using robust design method. The structural three-dimensional solid modeling of lower arm was developed using the Solidworks computer-aided drawing software. The threedimensional solid model then imported to the MSC.PATRAN software and employed to generate meshes and defined material properties for the finite element modeling. The linear elastic analysis was performed using NASTRAN codes. The optimization of lower suspension arm were carried out using stochastic design improvement based on Monte Carlo approach, Response surface methodology (RSM) based on central composite design (CCD) and artificial intelligent technique based on radial basis function neural network (RBFNN). Tetrahedral element with 10 nodes (TET10) and tetrahedral element with 4 nodes (TET4) mesh were used in the stress analysis. The modal analysis was performed with using Lanczos method to investigate the eigenvalue and mode shape. The highest von Mises stresses of TET10 were selected for the robust design parameter. The development from the Stochastic Design Improvement (SDI), RSM and ANN are obtained. The design capability to endure highest load with lower predicted stress is identified through the SDI process. CCD used to predict and assess linear response Von Mises and Displacement on Lower arm systems models. On the other hand, RBFNN used to investigate linear response of lower arm. It can be seen that the robust design was capable to optimize the lower vehicle arm by using stochastic optimization and artificial intelligent techniques. The developed linear model based on SDI and CCD is statistically adequate and can be used to navigate the design space. A new parameter of material can be reconsidered in order to optimize the design. The results can significantly reduce the cost and time to market, improve product reliability and customer confidence. These results can be use as guideline before developing the prototype.