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The twist-beam suspension is widely used in vehicles due to the simplicity of its construction, less occupied space and its low manufacturing cost in comparison with multi-link suspension. The difficulties related to the design of a twist-beam axle concern the large number of possible configurations for twist-beam profile and the stiffness adjustment of axle beam and suspension arms. However, design process can be done with the aid of multibody dynamics simulations, by testing several configurations in a virtual way. In this work, a simplified twist-beam suspension model is studied, and the influence of variation of its parameters is analyzed in its elastokinematics behavior and in handling performance of a vehicle.

There are many variables involved in the design of a front suspension, such as hardpoints' coordinates, steering geometry or even an anti-roll bar, which could make design difficult and time consuming. The MacPherson strut, due to the simplicity of its construction, less occupied space and low manufacturing cost, is widely used in vehicles in contrast to double wishbone and multi-link suspensions. Although its tuning process still demands time, it can be done with the aid of multibody dynamics simulations, by testing several configurations in a virtual way. In this work, a front suspension model with MacPherson strut is studied, so that the influence of variation of its parameters is analyzed in its elastokinematics behavior and in handling performance of a vehicle.

Optimization methods applied to problems of structural design, specifically in numerical problems in which are used the finite element method, working with a set of variables, allow to minimize (or maximize) a given objective function subject to certain design constraints [1]. Solving engineering problems often requires individual assessment of a large number of possible combinations for the design parameters that could be tested as potential solutions. Thus, it can be said that optimization methods provide directions to achieve solutions faster, significantly reducing product development times, without however eliminating the need for some iteration cycles in which it is comprised the phases of numerical simulation, results interpretation and feasibility analysis from a manufacturing viewpoint. Often, a new iteration cycle is done for the same optimization problem, starting from the final result of previous iteration cycle, until it is reached the so-called solution convergence.