Integrating graph neural networks with physics-informed loss function for mechanical response prediction of hollow concrete structures with morphed honeycomb configurations

Materials & Design, Год журнала: 2025, Номер unknown, С. 113659 - 113659

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial neural networks for inverse design of a semi-auxetic metamaterial

Thin-Walled Structures, Год журнала: 2024, Номер 200, С. 111927 - 111927

Опубликована: Апрель 20, 2024

Язык: Английский

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Inverse machine learning framework for optimizing gradient honeycomb structure under impact loading

Engineering Structures, Год журнала: 2024, Номер 309, С. 118079 - 118079

Опубликована: Апрель 27, 2024

Язык: Английский

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High Energy Absorption Design of Porous Metals Using Deep Learning

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 282, С. 109593 - 109593

Опубликована: Июль 27, 2024

Язык: Английский

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Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design

Advanced Materials, Год журнала: 2023, Номер 36(5)

Опубликована: Дек. 7, 2023

Abstract Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought‐after as efficient, renewable, and sustainable source, with the potential decrease reliance on traditional fossil fuels. However, developing triboelectric specific output remains a challenge mainly due uncertainties associated their complex designs for real‐life applications. Artificial intelligence‐enabled inverse design is powerful tool realize performance‐oriented nanogenerators. emerging scientific direction that can address concerns about optimization of leading next generation nanogenerator systems. perspective paper aims at reviewing principal analysis triboelectricity, summarizing current challenges designing optimizing nanogenerators, highlighting physics‐informed strategies develop Strategic particularly discussed in contexts expanding four‐mode analytical models by artificial intelligence, discovering new conductive dielectric materials, contact interfaces. Various development levels intelligence‐enhanced are delineated. Finally, intelligence propel prototypes multifunctional intelligent systems applications discussed.

Язык: Английский

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Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Май 4, 2024

Abstract In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an perspective, biomaterials, biomechanics, biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. this context, silico modelling stands out effective efficient alternative investigating complex interactive responses vivo. This paper offers comprehensive review of swiftly expanding machine learning (ML) techniques, empowering to develop cutting-edge treatments addressing healthcare challenges. The categorically outlines different types ML algorithms. It proceeds by first assessing their applications covering such aspects data mining/processing, digital twins, data-driven design. Subsequently, approaches are scrutinised studies on mono-/multi-scale biomechanics mechanobiology. Finally, extends techniques bioprinting biomanufacturing, encompassing design optimisation situ monitoring. Furthermore, presents typical ML-based implantable devices, including tissue scaffolds, orthopaedic implants, arterial stents. challenges perspectives illuminated, providing insights academia, industry, professionals further apply strategies future studies.

Язык: Английский

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Review of damping composite materials and structures involving self-healing constituents

Frontiers of Mechanical Engineering, Год журнала: 2025, Номер 20(2)

Опубликована: Апрель 1, 2025

Язык: Английский

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Periodic implicit representation, design and optimization of porous structures using periodic B-splines

Computer-Aided Design, Год журнала: 2024, Номер 171, С. 103703 - 103703

Опубликована: Март 11, 2024

Язык: Английский

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A prospective on machine learning challenges, progress, and potential in polymer science

MRS Communications, Год журнала: 2024, Номер 14(5), С. 752 - 770

Опубликована: Июль 1, 2024

Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science engineering every year. Polymer is no different, though implementation of data-driven algorithms this subfield has unique challenges barring widespread application these techniques the study polymer systems. In Prospective, we discuss several critical ML science, including structure representation, high-throughput limitations, limited data availability. Promising studies targeting resolution issues are explored, contemporary research demonstrating potential despite existing obstacles discussed. Finally, present an outlook for moving forward. Graphical

Язык: Английский

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Multiscale Experiments and Predictive Modeling for Failure Mitigation in Additive Manufacturing of Lattices

Advanced Materials Technologies, Год журнала: 2024, Номер unknown

Опубликована: Июль 28, 2024

Abstract Additive Manufacturing (AM) empowers the creation of high‐performance cellular materials, underscoring increasing need for programmable and predictable energy absorption capabilities. This study evaluates impact a precisely tuned fused filament fabrication (FFF) process on failure characteristics 2D‐thermoplastic lattice materials through multiscale experiments predictive modeling. Macroscale in‐plane compression testing both thick‐ thin‐walled lattices, along with their µ‐CT imaging, reveal relative density‐dependent damage mechanisms modes, prompting development robust modeling framework to capture process‐induced performance variation damage. For lower density an FE model based extended Drucker–Prager material model, incorporating Bridgman's correction crazing criteria, accurately captures crushing response. As increases, interfacial bead‐bead interfaces becomes predominant, necessitating enrichment microscale cohesive zone debonding. The introduces enhancement factor, offering straightforward method assess AM performance, thereby facilitating inverse design FFF‐printed lattices. approach provides critical evaluation how FFF processes can be optimized achieve highest attainable mitigate failures in architected materials.

Язык: Английский

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