Finite Element Simulations and Characterization of Airbags Using DOE

In the present study Low velocity impact tests have been carried out to study the energy absorption capabilities of airbags, the main objective is to increase the energy absorption capabilities by minimizing the acceleration levels and the number of bounces after the airbag impacts ground. In the present work, the effect of various parameters on the energy absorbing capabilities of airbag are studied. The three parameters chosen for the present study are Airbag Shape, Initial Pressure and the drop height. Finite Element Method (FEM) and Design of Experiments (DOE) approach are used in order to achieve the intended model objectives. The combination of both techniques is proposed to result in a reduction of the necessary experimental cost and effort in addition to getting a higher level of verification. It can be stated that the Finite Element Method coupled with Design of Experiments approach provides a good contribution in characterizing the airbag. The present work is divided into four phases, in the first phase Selection and testing of Airbag material is carried out, where the material required for the airbag is chosen and tested according to ASTM standards to find out the properties of the material. The second phase includes fabrication and testing of Airbags, Where three different kinds of airbags (circular, cylindrical and rectangular) having same surface area are fabricated and tested according to the Taguchi’s L9 Orthogonal Array experimental plan. In the third phase a Finite Element Model (FEM) which represents the drop tests on the airbags (carried out in the Low Velocity Impact Test Rig) is developed in order to evaluate the quality of the process parameters. In the fourth phase results are analyzed using Taguchi’s Signal to Noise ratio technique, for both experimental testing and simulation of airbags to analyze the data and for the prediction of optimum results. Finally the two methods are compared (FEM and Experimental) using DOE and the results are analyzed to get the optimal set of process parameters.