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This paper describes the application of statistical techniques known as Design of Experiments (D.O.E.) to efficiently use the results of numerical analysis data in order to improve the configuration of automotive systems. The general framework of these techniques is presented in a format aiming at the design engineer as their end user. Besides, a case study is presented with the purpose of illustrating their practical use. The first step of the case study is to build predictive models for the behaviour of the automotive system being developed by means of the Response Surface Method (RSM), using the proper D.O.E. options. Once these predictive models are available, automatic numerical optimization algorithms are used to improve the responses of interest for given operating conditions. Finally, the automotive systems are robust designed taking into account that the operating conditions vary randomly.

This paper describes the application of statistical techniques related to the condensation of computational models so that gradient based optimization procedures can be used more effectively. The adoption of these techniques is encouraged by the possibility of an important reduction in time and cost associated to the vehicle development process. A sophisticated computational model of a Mini-baja vehicle is defined in the virtual environment by means of CAD/CAE software, intending to provide the major information related to the study of its dynamic behaviour and to define the statistical surrogates (approximate models). The creation of the computational model deals with the determination of physical and geometric properties, and is fed by stiffness and damping parameters obtained through experimental procedures.

This work is concerned with the optimization of motorcycle suspension features, taking into account that several different operating conditions can occur. In order to have the suspension simultaneously optimized to a range of these conditions, its parameters are treated as deterministc design variables (in the classical automatic optimization sense) subject to the engineer's influence. Excitation parameters, on the other hand, are considered to be random or “noise” factors, out of the engineer's control. Once the structural and operational parameters are grouped into these categories, they are arranged in the form of combinatorial orthogonal arrays, and robust design techniques are employed to reach designs that besides being optimal, are imune to operating conditions variability. The characteristic robustness metrics used in this work are the Signal-to-Noise ratio (S/N) and the design capability index (Cdk).

For cast iron automotive parts, the automated shape optimization technique is a powerful design tool that usually enhances performance and reduces overall cost. Possessing the solid finite element model of what could be considered a starting point, first trial or initial design, commercially available structural synthesis software are able to optimally relocate the nodes, thus creating an optimal final shape. This is very suitable for parts obtained by metal melting for two main reasons: first, such components generally tend to be massive (posing the challenge to reduce weight) and second, the manufacturing method itself presents wide freedom regarding the shapes possible to be obtained (augmenting the chances the computationally generated shape is feasible in practice).

In the automated design of mechanical systems by means of structural synthesis techniques, two cathegories of procedures, namely shape and sizing optimization, play different but equally important roles: the former is usually applied to the synthesis of solid modelled (in the finite element method sense) structures, by finding optimum positions for the model's nodes, and the later is often used to optimize direct finite element properties (shell thicknesses and bar areas, for instance). Besides the nature of the finite element model (plane or solid) is mainly a consequence of the system's inherent features, not so seldom, the engineer can be faced with structures that allow both types of approaches and, for optimization purposes, the two aforementioned alternatives arise. Thus, in such situations, the choice for the type of finite element model to be built is also driven by which kind of structural synthesis (shape or sizing) would lead to better results.