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Analysis shows joint measurements of geometry and fracture morphology in metallic materials can improve prediction accuracy, suggesting a novel approach using Big Data algorithms.
The approaches to the prediction of the destruction of metallic materials with an inhomogeneous structure are analyzed. It is shown that joint measurements of the geometry of structural elements, their relative position (configuration) and fracture morphology at macro-, meso- and microscale levels are necessary to assess the risk of fracture, along with recording acoustic emissions taking into account the statistical nature of the studied objects. Obtaining representative results is possible based on the use of digital technologies and Big Data algorithms.
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Kudrya et al. (2022) studied this question.
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