On point cloud failure criterion predictions

Abstract

A new failure criterion has been developed to improve modeling of orthotropic structural composites subjected to quasi-static and impact loadings. Rather than using an analytical expression that traditionally has been employed to predict failure, a point cloud failure surface is constructed in the stress/strain space using a combination of virtual and laboratory testing. These discrete points are obtained by building a micromechanical model that is subjected to uniaxial and multiaxial states of stress until the first failure of a finite element in the model is detected. The post-peak response behavior is then activated till the element meets the erosion criterion and is deleted from the model. One of the challenges in using the generated point cloud data is the ability to correctly predict when failure onset in a finite element takes place without being too conservative. Three predictive methods are compared in this paper – Approximate Nearest Neighbor (ANN), Simplified Approximate Nearest Neighbor (SANN), and Neural Network (NN). Point cloud data from a unidirectional composite, the T800-F3900, commonly used for aerospace applications, is used for comparative evaluation of these methods. The performances are first evaluated using a standalone program not connected with FE analysis. Finally, two of these methods (SANN and NN) are implemented in a commercial finite element program, LS-DYNA, and their performances are evaluated by simulating a laboratory impact test. Results indicate that the SANN and NN implementations are robust, efficient, and accurate.