Modelagem da dinâmica vertical de coxins elastoméricos de motor através de método de redes neurais / Modeling vertical dynamic of elastomeric engine mounts using artificial neural networks

AUTOR(ES)
FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

03/10/2011

RESUMO

This dissertation scope is the modeling of an elastomeric mount, element which has primordial role on powertrain vibration isolation. The study of the mechanic behavior of those elastomeric materials is a field being studied for several years agor since its complexity and non-linarity of those components; and a reliable math models are competitive edge. So this dissertation has as main object purpose the modeling of an elastomeric engine mount using neural networks technician to predict the transfer function between the mount displacement and engine vertical accelerationTwo training approachs experimental data gathered in a uniaxial hydraulic bench and experimental data gathered in field are presented with the objective of identify the condition which the neural network presents better prediction performance. So this comparison being made a metric based on power density spectral area is presented to quantify the performance on the frequency range studied (0-40 Hz). Finally a comparison is made with a mechanical model composed per springs and dumpers combined. The results show that even the training made with the bench data and the mechnical model present good correlation from 0 Hz to 14 Hz while the training made with field data has good correlation from 0 Hz to 14 Hz and 23 Hz to 40 Hz once this approach has the ability to capture the hysteresis and part of rubber non-linearity. It is demonstrated that the frequency range where all the models are not capable to predict, there is a vibration mode where there is a node on the studied mount which dificults the modeling. The conclusion is that the neural network technique has great potential on its usage, presenting very satisfactory results, among other vantages as the processing speed after the network is trained.

ASSUNTO(S)

dinâmica veicular vertical dynamics coxins de motor coxins elastoméricos dinâmica vertical elastomeric mount engine mount modelagem modeling neural networks redes neurais vehicle dynamics

ACESSO AO ARTIGO

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