The mechanical properties of Ogden material under biaxial deformation are obtained by using the bubble inflation technique. First, pressure inside the bubble and height at the hemispheric pole are recorded during bubble inflation experiment. Thereafter, Ogden's theory of hyperelasticity is employed to define the constitutive model of flat circular thermoplastic membranes (CTPMs) and nonlinear equilibrium equations of the inflation process are solved using finite difference method with deferred corrections. As a last step, a neuronal algorithm artificial neural network (ANN) model is employed to minimize the difference between calculated and measured parameters to determine material constants for Ogden model. This technique was successfully implemented for acrylonitrile-butadiene-styrene (ABS), at typical thermoforming temperatures, 145°C. When solving for the bubble inflation, the recorded pressure is applied uniformly on the structure. During the process inflation, the pressure is not uniform inside the bubble, thus full gas dynamic equations need to be solved to get the appropriate nonuniform pressure to be applied on the structure. In order to simulate the inflation process accurately, computational fluid dynamics in a moving fluid domain as well as fluid structure interaction (FSI) algorithms need to be performed for accurate pressure prediction and fluid structure interface coupling. Fluid structure interaction solver is then required to couple the dynamic of the inflated gas to structure motion. Recent development has been performed for the simulation of gas dynamic in a moving domain using arbitrary Lagrangian Eulerian (ALE) techniques.
In this paper we present an inverse finite element approach for the simulation of anisotropic tube hydroforming operation. This method exploits the knowledge of the final part shape, by starting from this later we search for the nodal positions in the initial cylindrical tube which verify the equilibrium of the final part. We propose a geometrical initial solution allows avoiding the problem of vertical walls and reverse taper. Two numerical applications concerning the hydroforming of T and Y-shaped tubes made from welded low carbon steel AISI 1008-galvanized, using the flow stress data obtained from bulge test have been utilized to validate the method. Verifications of the obtained results have been carried out using the classical explicit dynamic incremental approach (EDIA) by ABAQUS® commercial code to show the efficiency of our approach.
Un critere macroscopique de plasticite des
milieux poreux ductiles a ete recemment formule par Monchiet et al. (2007) dans le cadre
des methodes d'analyse limite. Ce critere ameliore celui de Gurson (1977), en
particulier dans le domaine des faibles triaxialites de contraintes. La presente etude
vise a la mise en oeuvre numerique et l'evaluation d'un modele formule sur la base du
nouveau critere. Nous presentons d'abord la formulation complete de ce modele puis sa
numerisation dans un code de calcul. Nous discutons ensuite des resultats obtenus en les
comparant a des modeles existant.
The aim of this study is to develop a new method to predict the effective elastic and thermal behavior of heterogeneous materials using Convolutional Neural Networks CNN. This work consists first of all in building a large database containing microstructures of two phases of heterogeneous material with different shapes (circular, elliptical, square, rectangular), volume fractions of the inclusion (20%, 25%, 30%), and different contrasts between the two phases in term of Young modulus and also thermal conductivity. The contrast expresses the degree of heterogeneity in the heterogeneous material, when the value of C is quite important (C >> 1) or quite low (C << 1), it means that the material is extremely heterogeneous, while C= 1, the material becomes totally homogeneous. In the case of elastic properties, the contrast is expressed as the ratio between Young’s modulus of the inclusion and that of the matrix (C = EiEm), while for thermal properties, this ratio is expressed as a function of the thermal conductivity of both phases (C = λiλm). In our work, the model will be tested on two values of contrast (10 and 100). These microstructures will be used to estimate the elastic and thermal behavior by calculating the effective bulk, shear, and thermal conductivity values using a finite element method. The collected databases will be trained and tested on a deep learning model composed of a first convolutional network capable of extracting features and a second fully connected network that allows, through these parameters, the adjustment of the error between the found output and the expected one. The model was verified using a Mean Absolute Percentage Error (MAPE) loss function. The prediction results were excellent, with a prediction score between 92% and 98%, which justifies the good choice of the model parameters.
