Surrogate model of the thermoforming of fiber reinforced thermoplastics

Abstract

Abstract. In the thermoforming process shearing of fiber reinforced thermoplastics (FRTP) leads to wrinkle formation and is therefore one of the most critical deformation mechanism. The target value to predict wrinkle formation is the shear angle which is considered in this paper. To predict the shear angle distribution during the thermoforming of FRTPs a Convolutional Neural Network (CNN) is used. The approach is based on the consideration of the principal curvature characteristics, resulting in the reduction of any complex geometry to planar, parabolic, elliptical, semi-spherical and hyperbolic areas. In similar research, CNNs are trained for each specific geometry. In this work, the CNN refers to a database of the mentioned curvature characteristics. In theory, all forming geometries can be created from these, which is why the CNN is enabled to predict the forming result of any complex geometry. In this paper, the benchmark geometry of a double dome is selected as the validation geometry and the error in the prediction of the shear angle is compared with the results of numerical forming simulations performed.