Particle-reinforced composites are widely used in industry, primarily due to their versatile fabrication methods and the ability to tailor their properties. In many cases, extensive experimental campaigns are required to determine the optimal characteristics of the system to enhance specific propert...

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Main Authors: García, Israel García, Justo, Jesús, Zurita Van-Dinter, Alejandro, Mantič, Vladislav
Format: Article
Language:English
Published: Académie des sciences 2025-05-01
Series:Comptes Rendus. Mécanique
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Online Access:https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.293/
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Summary:Particle-reinforced composites are widely used in industry, primarily due to their versatile fabrication methods and the ability to tailor their properties. In many cases, extensive experimental campaigns are required to determine the optimal characteristics of the system to enhance specific properties. Micromechanical models can serve as a useful alternative or initial approach during the material design process. One of the easiest characteristics that can be modified is the size of the reinforcement, which, according to some models and preliminary evidence, can significantly affect the mechanical properties of the material.The objective of this work is to experimentally evaluate the size effect of reinforcement on the composite strength and to compare it with the predictions by the coupled criterion of finite fracture mechanics (CC-FFM). A secondary objective is to visualize the initiation of the failure mechanism, which starts at the particle-matrix interface and progresses toward a crack that splits the specimen. To achieve this, a new specimen design is proposed along with an optimized fabrication procedure. The tests were recorded using a high-speed camera, which allowed for the visualization of crack initiation at the particle-matrix interface.The experimental results show a strong size effect, where smaller particles correspond to higher apparent strength. The results are in relatively good agreement with the predictions of the CC-FFM.
ISSN:1873-7234