Dynamic Fracture-Toughness Testing of a Hierarchically Nano-Structured Solid

Kim, Kyung-Suk; Jin, Hanxun; Jiao, Tifeng; Clifton, R. J.

doi:10.1007/978-3-030-60959-7_16

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…The emerging development of nanotechnology and biotechnology in recent several decades continuously requires a new understanding of micro-and nano-scale material behaviors. For example, much research has been conducted on understanding the mechanical properties of thin films [177,178,179,180,181,182,183,184,185], nanos-tructured metals [186,187,188,189,190], sub-micron-sized sensors [191], crystalline nanowires [192,193,194,195,196,197], 2D materials [198,199,200,201,202], origami [203], nanolattice metamaterials [204,205,206], and copolymers with nanoscale features [53,207,208,209]. Conducting precise experiments to characterize these materials with small-scale features is essential to understand their properties, underlining mechanisms, and develop constitutive models.…”

Section: Micro and Nano-mechanicsmentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano-and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

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Section: Micro and Nano-mechanicsmentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…[ 11 ] This hierarchical architecture is also attributed to strengthening mechanisms at the nanoscale and is responsible for the scale‐dependent relaxation and dissipation contrivances associated with dynamic loading scenarios. [ 15 ] These nanoscale processes are also directly linked to the remarkable improvement in the dynamic toughness of polyurea [ 16 ] and are forecasted to apply to the broader class of elastomeric polymers.…”

Section: Introductionmentioning

confidence: 99%

Light‐Matter Interactions Revealing Load‐Induced Phase Mobility in Elastomers

Huynh

Koohbor

Youssef

2023

Macromol. Rapid Commun.

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Elastomers with segmental microstructure are a fascinating class of shock‐tolerant and impact‐resistant materials. However, their technological potential remains untapped due to a vague understanding of the molecular contributions to their superior mechanical behavior. Herein, in situ light‐matter interactions, to reveal the extent of microstructural mobility by temporally exploiting molecular processes during creep response, are leveraged. The segmental microstructure comprises aromatic hard domains embedded within an aliphatic soft matrix. High‐resolution digital image correlation reveals the development of strain striations, mild anisotropy, and the mechanisms responsible for domain mobility, where the rate of hard segment mobility is found to be 60% slower than that of the soft segment. Terahertz spectral analyses pinpoint the contributions of interchain hydrogen bonding of the hard segments and their significant conformational changes by observing spectral features at ≈1.2THz and ≈1.67THz. Moreover, the domain mobility is examined using experimental and computational light scattering approaches, uncovering dynamic scattering and elucidating the difference in the complex refractive index of the soft and hard segments. The study unlocks the pathway for quantitative measurements of elusive molecular mobility and conformational changes during mechanical loading and sheds light on the origin of the shock tolerance in some elastomeric polymers with segmental microstructure.

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Mechanical Metamaterials Fabricated From Self-Assembly: A Perspective

Jin,

Espinosa

2023

Journal of Applied Mechanics

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Mechanical metamaterials, whose unique mechanical properties stem from their structural design rather than material constituents, are gaining popularity in engineering applications. In particular, recent advances in self-assembly techniques offer the potential to fabricate load-bearing mechanical metamaterials with unparalleled feature size control and scalability compared to those produced by additive manufacturing (AM). Yet, the field is still in its early stages. In this perspective, we first provide an overview of the state-of-the-art self-assembly techniques, with a focus on the copolymer and colloid crystal self-assembly processes. We then discuss current challenges and future opportunities in this research area, focusing on novel fabrication approaches, the need for high-throughput characterization methods, and the integration of Machine Learning (ML) and lab automation for inverse design. Given recent progress in all these areas, we foresee mechanical metamaterials fabricated from self-assembly techniques impacting a variety of applications relying on lightweight, strong, and tough materials.

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Dynamic Fracture-Toughness Testing of a Hierarchically Nano-Structured Solid

Cited by 3 publications

References 2 publications

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Light‐Matter Interactions Revealing Load‐Induced Phase Mobility in Elastomers

Mechanical Metamaterials Fabricated From Self-Assembly: A Perspective

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