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Abstract.A complete three-dimensional multi-body dynamic computational model of the human head and neck has been developed and validated using human volunteer experimental data. The complete head-neck model has been used to simulate 15g frontal and 8.5g rear-end impacts with the resulting motion compared against response corridors derived from sled acceleration tests using human volunteers. This paper reports an original work, a further development of the model that incorporates a finite element analysis of the intervertebral discs subjected to the loading conditions determined by the multi-body dynamic model of the head and neck complex.

This article presents the effects of the frontal and rear-end impact loadings on the cervical spine components by using a multibody dynamic model of the head and neck, and a viscoelastic finite element (FE) model of the six cervical intervertebral discs. A threedimensional multi-body model of the human head and neck is used to simulate 15 g frontal and 8.5 g rear-end impacts. The load history at each intervertebral joint from the predictions of the multi-body model is used as dynamic loading boundary conditions for the FE model of the intervertebral discs. The results from the multi-body model simulations, such as the intervertebral disc loadings in the form of compressive, tensile, and shear forces and moments, and from the FE analysis such as the von Mises stresses in the intervertebral discs are analysed. This study shows that the proposed approach that uses dynamic loading conditions from the multi-body model as input to the FE model has the potential to investigate the kinetics and the kinematics of the cervical spine and its components together with the biomechanical response of the intervertebral discs under the complex dynamic loading history.

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Sheet metal bending is one of the most frequently used sheet metal forming processes in manufacturing industry. This study investigates bending parameters and springback phenomenon of a stainless-steel sheet in air bending process. In most of the applications, springback is determined either by trial and error procedures or by using numerical methods. Artificial Neural Network (ANN) approach has proved to be a helpful tool for the engineers. ANN is used in this study to predict the springback amounts of stainless steel sheets through experiment based networks. Air bending process is first modeled and analyzed by a commercial finite element code. Springback amounts for different sheet thicknesses and bend angles are computed. In addition to computational modeling, experimentation of the air bending processes is carried out and experimental results are used in artificial neural network development to show the feasibility of ANN based on experimentation. Experimental outcome is also used for validation of the FE analysis of the process, which demonstrates good agreement. It is observed that ANN can be applied effectively to determine springback in air bending process, which embodies significant potential to determine air bending process parameters for industrial applications such as punch stroke.