Modified micro-mechanics based multiscale model for progressive failure prediction of 2D twill woven composites

To consider fiber random distribution at the microscale for the multiscale model based on the micro-mechanics failure (MMF) theory, clustering method is used for the extraction of amplification factors. As the clustering method is a kind of unsupervised machine learning method, the elements with similar mechanical behavior under external loading can be included in a cluster automatically at the microscale. With this modification, the fiber random distribution model can be used for multiscale damage analysis in the framework of MMF theory. To validate the modified multiscale analysis method, progressive damage analysis of a kind of 2D twill woven composites is conducted based on different microscale models. The stress values for microscale models with fiber hexagonal and random distribution patterns are compared first. Much higher stress concentration is generated in the fiber random distribution model due to the smaller inter-fiber distance especially under longitudinal shear loading. The obtained cluster distribution results exhibit the characters of the stress distribution in the two microscale models. Thereafter, tensile and compressive responses of the 2D twill woven composite are predicted with the modified multiscale analysis method and accuracy of the method is verified through comparison with published experimental results. From the simulation results, it can be found that the matrix damage initiation from the model based on the fiber random distribution model is premature compared with that from the model based on the fiber hexagonal distribution model. Besides, under tensile loading, the damage all initiates from the fill tows and propagates to the wrap tows. However, under compressive loading, the matrix damage initiates from the wrap tows in the model based on the fiber random distribution model.