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Due to commercial television almost everyone is familiar ‘automotive inflatable restraint system,’ commonly referred to as airbag. Traditionally these bags were made of polyamide fabrics coated with polychloroprene, which made them essentially impermeable. Even though this restraint technology has been in use for more than fifteen years, there remain some features that still need to be improved; i.e., the high cost, the high package volume, the weight and the need for replacement of coated fabrics. In this paper special attention is given to uncoated fabrics. A novel blister-inflation technique was utilized to evaluate the permeability of test fabrics under biaxial stretching conditions. Further, the effect of inflation temperature and internal pressure drop across the fabric on the permeability of the fabrics can be evaluated by this technique.

Safety restraint technology relies on woven fabrics as the principle material of construction. On impact, gases are generated instantaneously to inflate the bag. As the pressure within the bag increases during deployment and later from passenger contact, the airbag fabric stretches in a biaxial-manner. Passenger contact with the slowly deflating airbag accelerates the gaseous outflow through the fabric, airbag seams, and through specially constructed vents. A fraction of the impact energy can also be adsorbed by mechanical biaxial stretching of the fabric's fibers. However, the fabric's permeability and/ or vent system appear to be of primary importance to energy dissipation. A unique blister-inflation technique was developed and used to evaluate the fabric properties necessary for energy dissipation by these four mechanisms. The performance of fabrics woven from two traditional commercial polymeric fibers offered for airbag construction were considered.

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.