Ramesh Keshavaraj

Airbag fabric material modeling of nylon and polyester fabrics using a very simple neural network architecture

The material properties of engineering fabrics that are used to manufacture airbags can not be modeled easily by the available nonlinear elastic–plastic shell elements. A nonlinear membrane element that incorporates an elaborate tissue material model has been widely used by the auto industry for the airbag simulation studies. This model is highly computation intensive and does not differentiate between the various physical properties of the fabrics like fiber denier, the polymer fiber, and weave pattern. This paper introduces a new modeling technique that uses artificial neural networks. Experimental permeability data for fabrics under biaxial strain conditions were obtained through a blister-inflation technique and were used to train the proposed network architecture. In this training environment, various properties of the fabric can be incorporated and the network can be trained to generalize relative to the environment. Once trained, the cause–effect pattern is assimilated by the network with approprate weights to produce a desired output. Fabrics tested in this study included nylon 66 fabrics with three different fabric deniers: 420, 630, & 840 and two types of weave, and two 650-denier polyster fabrics having different calendering effects. The predictions obtained from this neural network model agreed very well with the experimental data. This indicates that neural nets can be considered as a serious design tool use in determining permeability and biaxial stress–strain relationships for textile fabrics used in airbags.

Changes in Crosslink Density of Structural Silicone Sealants Due to Ozone and Moisture

Abstract The changes in the crosslink density, elastically effective chains and entanglements in the network, and the molecular weights between crosslinks of three different commercially available structural silicone sealants used in glazing applications were monitored as a function of specific aging factors. The aging variables included simultaneous exposure to ozone and moisture in which the pH of the moisture was maintained at discrete levels in the acid and the alkaline range. The simultaneous effect of external loading on the performance of these sealants was also studied. Unexposed and unstressed test samples kept at ambient laboratory conditions were used as the control. Results of these weathering studies indicate that the crosslink density of the sealants will decrease during the initial period as a result of their exposure, and some surface cracking was observed. However, following this initial exposure period, the sealants showed an increase in the crosslinking activity. All three formulations ...

Analysis of Fabrics used in Passive Restraint Systems – Airbags

This paper concerns the permeability and biaxial stress–strain behavior of woven fabrics used in the construction of automotive airbags. During deployment, gases are generated which inflate the airbag, distend it, and then energy is dissipated by the viscous flow of air forced to pass through the fabric. In order to quantify this latter behavior, the permeability of samples of expansible fabric was correlated with biaxial stress–strain behavior using a ‘blister-inflation’ technique. This paper presents the laboratory data obtained from two different commercial fabrics, nylon 66 and polyester, offered for use in the manufacture of automobile airbags. Overall, permeability measurements were made on the fabric samples for 15 different isobaric pressure drops (3.44 kPa to 103.42 kPa) and five different temperatures ranging from 8°C to 95°C. Both temperature and pressure were found to affect the permeability of the fabrics. For both types of fabrics tested, the maximum permeability was observed to coincide wit...

A realistic comparison of biaxial performance of nylon 6,6 and nylon 6 fabrics used in passive restraints—airbags

Nylon has been the material of choice for airbag construction because of its specific strength and dimensional stability during deployment. Of the nylons, nylon 6,6 has been widely used in airbag construction. In this article, we attempted to compare the performance of several commercial nylon 6,6 and nylon 6 fabrics offered, for use, to the auto industry. The performance of four traditional nylon 6,6 fabrics are compared with identical fabrics made from nylon 6 fibers. We used a test procedure championed by Chrysler but was developed in our laboratory called the blister-inflation. This test mimics the biaxial deformation of airbag fabric in a manner similar to the deformation of airbag fabric during actual deployment. Several other engineering properties of interest in airbag application are also addressed in this article for comparison purposes.

COMPARISON OF CONTRIBUTIONS TO ENERGY DISSIPATION PRODUCED WITH SAFETY AIRBAGS

In this paper a unique blister-inflation technique is described. This technique was used to evaluate the fabric properties necessary for energy dissipation. The performance of fabrics woven from two traditional commercial polymeric fibers offered for airbag construction were examined: (1) a traditional polyamide nylon 66; and (2) a high strenth poly(ethylene terephthalate). These two fabrics, with differences in fiber denier and weave, were evaluated for five different inflation temperature levels and at eleven different pressure drops. A kinetic-energy model was developed to account for the energy that should be dissipated by four different mechanisms. The paper focuses on and compares airbags made from the polymeric fibers which contain no vents. Based on the proposed model predictions, existing airbags appear to be adequate in their ability to dissipate energy over a rather short time frame. For the covering abstract of the conference see IRRD 875833.

Effects of moisture on structural silicone rubber sealants used in window glazing applications

When a structural sealant is exposed to moisture, a number of phenomena are known to occur which may cause it to deteriorate. The degree of deterioration, however, is a function of various parameters such as the type of sealant, the time of curing, etc. Silicone sealants are widely used for structural glazing applications because of their good resistance to weathering. In this study, moisture effects on commercial silicone sealants were monitored by observing changes in their physical properties. The moisture was applied as a water spray in which the pH of the water ranged from 3 to 11. The extremes of the pH range represent recorded values for acid rain (pH of 3) and basic cleaning solutions (pH of 11). Laboratory evaluations of deterioration were based on changes in tensile tangential modulus, percentage elongation and ultimate tensile stress.

Modeling of Crosslinking Mechanism When Structural Silicone Sealants Are Subjected to Moisture

Abstract Most polymeric sealants used in structural applications are organic and so some changes in the materials occur with passage of time: the material ages. The structural sealants function as a weather seal and when used with glazing also act as a binding agent for the glass to the substrate. As a weather seal, the sealants will be exposed to a wide variety of service conditions which include moisture. A question arises, therefore, as to the damage caused by moisture. When a structural sealant is exposed to moisture, a number of distinct phenomena are known to occur which can contribute to deterioration in the performance of the seal. In this study the moisture effect on the structural silicone sealant was correlated through crosslinking changes. A typical weathering event consisted of exposing the test coupons to moisture with pH levels ranging from 3 to 11, followed by drying. The extremes of the pH levels simulated acid rain (pH = 3) and cleaning solutions (pH = 11). Experimental data suggest that...

Simple numerical model (PRAM) for simulation of the passenger-bag interactions during deployment of an airbag

The pressure-time history of a deployed airbag provides the basis for the restraint created by this safety system. A simple numerical simulation of this pressure–time history was developed based on our understanding of the various factors thatinfluence the restraint performance. The general interaction forces between the passenger and the airbag can be analyzed using this model. This article discusses some of the complex issues pertaining to the interaction forces between the occupant and the airbag. The predictions provided by the proposed numerical passenger restraint action model are in good agreement with published, experimental data.

MODELING OF BIAXIAL DEFORMATION OF AIRBAG FABRICS USING ARTIFICIAL NEURAL NETS

This study used a feed-forward artificial neural network (ANN) technique in order to predict fabric permeability. The study introduced a set of input patterns on the fabric permeability during the ANN training phase. 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 behaviour. 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. The study indicates that ANN can be an effective tool in modeling airbag fabric behaviour. This process 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. For the covering abstract of the conference see IRRD 875833.

Fluid Property Prediction of Siloxanes with the Aid of Artificial Neural Nets

It is a time-consuming task to develop models or empirical equations which would accurately predict fluid properties of polymers as a function of chemical constitution and other experimental variables. Consequently, extensive experimental data have been published in the literature. When such experimental data are available, considerable time can be saved by the use of artificial neural network (ANN) technique based model approaches. Even though these ANNs have been in existence for a long time, their use in predicting fluid properties is still rather limited. In this paper, an attempt has been made to predict the fluid properties of different siloxanes as a function of their molecular configurations with the aid of ANN.