Optimization in design parameters of mechanical systems using multi-objective genetic algorithm / Otimização de parametros de projeto de sistemas mecanicos atraves de algoritmo genetico multi-objetivos

AUTOR(ES)
DATA DE PUBLICAÇÃO

2007

RESUMO

The mechanical systems are designed to be applied to any specific situations, and in this waytheir features should be measured to guarantee confidence to the systems. Their development and analysis expose the designer to a series of unknown parameters from several sources such as material properties, environmental and operational conditions. In terms of mathematical modeling, the inherent approximation and hypotheses made during system conception lead to different responses obtained by simulations and/or experimental measurements. So, in a previous phase of mathematical modeling, during the design analysis, the application of statistical tools and optimization methods is possible to estimate the values and/or ranges of the critical design parameters inside an experimental space. The connection between optimization and statistical data back at least to the early part of the 20th century and encompasses many aspects of applied and theoretical statistics, including hypothesis testing, parameter estimation, model selection, design of experiments and process and product control. So, this work proposes a link between theory of design of experiments, response surface methodology and multi-objective optimization using genetic algorithms, in order to optimize parameters for mechanical components. This study makes possible to verify the application of multi-objective optimization using genetic algorithms in design parameters and optimize them. A rotor-bearing system was used and amplitude in frequency domain was minimized. An experimental software for multi-objective optimization using genetic algorithm was developed

ASSUNTO(S)

multi-objective optimization maquinas - vibração response surface methodology genetic algorithm rotating machines algoritmos geneticos otimização matematica planejamento experimental

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