GL
Gilles Lubineau
9 records found
1
Physics-Informed Neural Networks (PINNs) are computationally efficient tools for addressing inverse problems in solid mechanics, but often face accuracy limitations when compared to traditional methods. We introduce a refined PINN approach that rigorously enforces certain physics
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Extrinsic toughening in bonded joints with hybrid thermoset–thermoplastic bondline
Experimental evidence and modeling strategy
Adhesive bonded composite joints with an embedded insert consisting of an interfacial hybrid thermoset–thermoplastic bondline could activate an extrinsic toughening mechanism that quadruples the mode I fracture toughness. However, the mechanisms of extrinsic toughening (anchoring
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Metamaterials are a cla b of materials with extraordinary capabilities derived from their engineered structure. In 2017, Frenzel et al. (Science 358, 2017) conceptualized "three-dimensional mechanical metamaterials with a twist,"which convert linear deformation into rotational mo
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Existing interface models are often inaccurate when modeling delamination of Fiber-Reinforced Polymer (FRP) structures, because they do not account for the non-local effects resulting from extrinsic failure mechanisms. Indeed, local traction separation laws are valid only for sim
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Harnessing Extrinsic Dissipation to Enhance the Toughness of Composites and Composite Joints
A State-of-the-Art Review of Recent Advances
Interfaces play a critical role in modern structures, where integrating multiple materials and components is essential to achieve specific functions. Enhancing the mechanical performance of these interfaces, particularly their resistance to delamination, is essential to enable ex
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Multiprocess additive manufacturing (MPAM) unlocks new materials and design spaces where multimaterial components consisting of polymers, metals, and ceramics can be produced as one consolidated part. MPAM enables state-of-the-art 3D-printed electronics and devices with embedded
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Metamaterials possess properties not found in nature and are expected to revolutionise the design of structural components. However large-scale production of metallic metamaterials remains locked due to the compromise between print size and resolution in existing metal 3D printin
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This paper proposes a data-driven method to predict mechanical responses for structures directly from full-field observations obtained on previously tested structures, with minimal introduction of arbitrary models. The fundamental concept is to directly use raw data, called patch
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Carbon fiber-reinforced polymers (CFRPs) have widely attracted the aerospace and automotive industries due to high stiffness and lightweight. Secondary adhesive bonding of CFRPs is a promising research field to fully explore their potential. However, multiple challenges have limi
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