Generating Point Cloud Failure Surface of Polymeric Unidirectional Composites Using Virtual Tests

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Description
Composite materials have gained interest in the aerospace, mechanical and civil engineering industries due to their desirable properties - high specific strength and modulus, and superior resistance to fatigue. Design engineers greatly benefit from a reliable predictive tool that can

Composite materials have gained interest in the aerospace, mechanical and civil engineering industries due to their desirable properties - high specific strength and modulus, and superior resistance to fatigue. Design engineers greatly benefit from a reliable predictive tool that can calculate the deformations, strains, and stresses of composites under uniaxial and multiaxial states of loading including damage and failure predictions. Obtaining this information from (laboratory) experimental testing is costly, time consuming, and sometimes, impractical. On the other hand, numerical modeling of composite materials provides a tool (virtual testing) that can be used as a supplemental and an alternate procedure to obtain data that either cannot be readily obtained via experiments or is not possible with the currently available experimental setup. In this study, a unidirectional composite (Toray T800-F3900) is modeled at the constituent level using repeated unit cells (RUC) so as to obtain homogenized response all the way from the unloaded state up until failure (defined as complete loss of load carrying capacity). The RUC-based model is first calibrated and validated against the principal material direction laboratory tests involving unidirectional loading states. Subsequently, the models are subjected to multi-directional states of loading to generate a point cloud failure data under in-plane and out-of-plane biaxial loading conditions. Failure surfaces thus generated are plotted and compared against analytical failure theories. Results indicate that the developed process and framework can be used to generate a reliable failure prediction procedure that can possibly be used for a variety of composite systems.
Date Created
2021
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