EasyPBC is an ABAQUS CAE plugin developed to estimate the homogenised effective elastic properties of user created periodic representative volume element (RVE), all within ABAQUS without the need to use third-party software. The plugin automatically applies the concepts of the periodic RVE homogenisation method in the software's user interface by categorising, creating, and linking sets necessary for achieving deformable periodic boundary surfaces, which can distort and no longer remain plane. Additionally, it allows the user to benefit from finite element analysis data within ABAQUS CAE interface after calculating homogenised properties. In this article, the algorithm of the plugin based on periodic RVE homogenisation method is explained, which could be developed for other commercial FE software packages. Furthermore, examples of its implementation and verification are illustrated.
This study investigates the effect of micro-scale geometric and material property uncertainties on the elastic properties and reliability of fibre reinforced composite materials. Composite materials are often designed using conservative design factors to account for a limited understanding of how multiscale uncertainties effect reliability. Structural reliability analysis can produce more efficient designs, but requires an understanding of how all sources uncertainty effect probability of failure. Previous studies have not considered micro-scale geometrical uncertainties and their combinations in a multiscale probabilistic-based reliability framework. Thus, this study will investigate the effect of numerous combinations of micro-scale material property and geometric uncertainties on the homogenised elastic properties. Furthermore, to account for the effect in a reliability-based framework, a novel surrogate modelling technique is developed to represent the uncertainties efficiently. The study concluded that the geometrical fibre stacking uncertainty is as influential as the widely investigated constituent material stiffness uncertainties. Consequently, representing the micro-scale geometric uncertainties within the developed multi-scale probabilistic-based framework improves the estimated stiffness. Thus probability of failure is reduced, compared with considering material property uncertainties only. Moreover, the framework clarified and highlighted the importance of representing fibre geometrical stacking uncertainty for a deeper understanding of their effect on composite stiffness properties.
The increasing use of fibre reinforced polymer composite materials in a wide range of applications increases the use of similar and dissimilar joints. Traditional joining methods such as welding, mechanical fastening and riveting are challenging in composites due to their material properties, heterogeneous nature, and layup configuration. Adhesive bonding allows flexibility in materials selection and offers improved production efficiency from product design and manufacture to final assembly, enabling cost reduction. However, the performance of adhesively bonded composite structures cannot be fully verified by inspection and testing due to the unforeseen nature of defects and manufacturing uncertainties presented in this joining method. These uncertainties can manifest as kissing bonds, porosity and voids in the adhesive. As a result, the use of adhesively bonded joints is often constrained by conservative certification requirements, limiting the potential of composite materials in weight reduction, cost-saving, and performance. There is a need to identify these uncertainties and understand their effect when designing these adhesively bonded joints. This article aims to report and categorise these uncertainties, offering the reader a reliable and inclusive source to conduct further research, such as the development of probabilistic reliability-based design optimisation, sensitivity analysis, defect detection methods and process development.
In this paper, a Finite Element-based surrogate model is developed to efficiently estimate stiffness properties of unidirectional composite laminas, while accounting for geometric and material property uncertainties at micro, meso and laminate scale, all within a probabilistic framework. In the multi-scale build-up nature of composites, uncertainties occur in material properties and geometric characteristics. These uncertainties present a challenge in estimating composite material properties. The currently available property estimation/homogenisation tools are mainly divided into two categories: analytical methods constrained by configuration assumptions, and numerical homogenisation using Finite Element Analysis (FEA). The latter is more flexible and accurate, but computationally expensive. Hence, this paper develops a surrogate model based on a limited number of experimental FEA data points. Additionally, a transition phase is developed between micro and laminate scales that enables modelling of spatially varying uncertainties. As a result, this framework significantly decreases analysis duration compared with FEA techniques, and, because it is derived from FEA data points, can accurately represent a wide range of uncertainties.
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