Modeling of Biaxial Deformation of Airbag Fabrics Using Artificial Neural Nets

Paper #:
  • 950343

Published:
  • 1995-02-01
Citation:
Keshavaraj, R., Tock, R., and Nusholtz, G., "Modeling of Biaxial Deformation of Airbag Fabrics Using Artificial Neural Nets," SAE Technical Paper 950343, 1995, https://doi.org/10.4271/950343.
Pages:
14
Abstract:
Supplemental airbag safety restraint systems are an integral part of today's vehicle package. This inflatable restraint technology relies heavily on woven fabrics and particularly on knowledge pertaining to a fabric's permeability as a function of pressure drop, inflation temperature of the gas and fabric weave. While fabric permeability can be quantified by actual experimental measurements, the number and non-linearity of the variables involved make the experiments time and cost intensive. Moreover, interpolations within a given data set yield questionable results.For these reasons a feed-forward artificial neural network (ANN) technique was utilized to predict fabric permeability. This is an interpretive procedure. An ANN routine must first be trained. During this training the ANN is introduced to actual cause and effect patterns with adjustments being made by changes in weighting factors until the errors in the output variables are minimized. Once trained, ANN can ascertain the essentials of the relationships and do so automatically.In this study, a set of the input patterns on the fabric permeability was introduced during the ANN training phase. Then the individual weights for the interconnections between nodes were adjusted until the inputs of temperature, pressure drop and fabric type yielded the required permeability output. In this way the ANN learned the desired input-output response behavior. After the initial training, the ANN was tested on additional data that were not part of the training processes. The predictions of the trained network agreed very well with these new experimental data.This study indicates that ANN can be an effective tool in modeling airbag fabric behavior. This processes requires time and experimental data for training. However, once trained, only fractions of a second are needed for information assimilation and output generation. This coupled with simplicity of use and accuracy of predictions make ANN attractive for on-line applications.
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