This article presents a detailed analysis on stress concentration in notched unidirectional fiber-reinforced composites. Due to the formation of longitudinal splitting at notch tips along the fiber direction, the extremely high stress concentrations ahead of the notch tips could be drastically reduced for composites under remote tension. The inability of the widely used material property degradation method to accurately redistribute the local stresses at the notch tips is examined. The notch blunting effect is investigated by modeling the longitudinal splitting as a thin plastic cohesive layer or debonding, and results for the stress redistribution in the unnotched section directly ahead of the notch tips are presented. By introducing the intra- and inter-ply damage modes, the failure of a double-notched cross-ply laminate is predicted and compared with the experimental results from open literature.

In early loading stage of composite laminates, longitudinal splitting in terms of matrix cracking usually initiates from notch tips and propagates in fiber directions. The extremely high stress concentrations at the notch tips could be drastically alleviated by the splitting. An efficient finite element model is created in this article to perform progressive failure analyses on notched composite laminates. To capture the true stress concentrations at notch tips, surface-based cohesive contacts instead of cohesive elements were introduced to model the splitting. The advantage of the surfaced-based cohesive contact over the traditional cohesive element is that it allows different mesh configurations for different plies. Predicted failure patterns and tensile strengths by the finite element model were compared with tested results from open literature.

Residual stress within a structural component can significantly affect the mechanical performance and stability of a structure. Therefore, it is crucial to find a way to determine the residual stress distribution to maintain the normal working of structures. Conventional methods for residual stress determination primarily include experimental testing, finite element simulations and inverse identification. However, these methods suffer from disadvantages of high testing costs, long calculation time and low inverse efficiency. To avoid these shortcomings, this study developed a high-performance method based on a deep learning technique. In this method, an artificial neural network was used to replace the finite element calculation in the finite element model updating (FEMU) technique and the residual stress distribution of structural components was inversely obtained based on the measured residual stresses of a finite number of measuring points. Compared with the conventional FEMU technique, the calculation efficiency of the proposed method was considerably improved. Furthermore, the accuracy and efficiency of the method were verified by simulated four-point bending experiments considering an elastic-plastic material.

Digital image correlation (DIC) is a widely used photomechanical method for measuring surface deformation of materials. Practical engineering applications of DIC often encounter challenges such as discontinuous deformation fields, noise interference, and difficulties in measuring boundary deformations. To address these challenges, a new, to the best of our knowledge, DIC method called MCNN-DIC is proposed in this study by incorporating mechanical constraints using neural network technology. The proposed method applied compatibility equation constraints to the measured deformation field through a semi-supervised learning approach, thus making it more physical. The effectiveness of the proposed MCNN-DIC method was demonstrated through simulated experiments and real deformation fields of nuclear graphite material. The results show that the MCNN-DIC method achieves higher accuracy in measuring non-uniform deformation fields than a traditional mechanical constraints-based DIC and can rapidly measure deformation fields without requiring extensive pre-training of the neural network.