Experimental data-driven uncertainty quantification for the dynamic fracture toughness of particulate polymer composites

Sharma, Aanchna; Mukhopadhyay, T.; Kushvaha, Vinod

doi:10.1016/j.engfracmech.2022.108724

Cited by 12 publications

(3 citation statements)

References 54 publications

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“…Furthermore, quantifying uncertainty for inhomogeneous materials like composites under dynamic loading is crucial to ensure an accurate assessment of dynamic fracture properties. Sharma et al [147] developed an ML framework bridging limited experimental data from advanced experimental techniques and data-driven models like Monte Carlo simulation to quantify the uncertainty of the dynamic fracture toughness of glass-filled epoxy composites.…”

Section: For Fracture 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: For Fracture 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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“…Different techniques have been developed to determine the dynamic fracture toughness of polymers, some of them based on the split Hopkinson pressure bar [ 14 ] or in dynamically loaded three-point bending specimens [ 15 , 16 ], both in combination with high-speed cinematography for capturing real-time crack initiation and propagation. Fond & Schirrer [ 17 ] and Kopp et al [ 18 ] carried out dynamic fracture tests on strip band specimens to explore the brittle behavior of rubber toughened PMMA during rapid crack propagation.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Fracture Resistance under Plane Strain Conditions of High-Density Polyethylene Nanoclay Composites

et al. 2023

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Polymer nanoclay composites have received significant attention due to their substantially enhanced mechanical, thermal and barrier properties. However, the effect of these nanoclays on the dynamic fracture resistance of a polymer matrix during fast fracture events has not been documented. In this study, the effect of nanoclay addition on the rapid crack propagation (RCP) resistance of high-density polyethylene (HDPE) was investigated through the high-speed double torsion test. Results showed that the addition of 1, 3, and 5% of nanoclays improved the dynamic fracture resistance under the plane strain conditions (Gd1) of HDPE up to 65%. An increase in the storage and loss modulus, and a decrease in crystallinity and melt flow index with nanoclay content was also found. Although the presence of agglomerates can hinder the enhancement of Gd1 as it promotes agglomerate fracture and debonding, the increase in energy consumption through fibrillation and crazing promoted by the nanoclay prevails, suggesting that the nanoclay’s toughening effect that has been extensively reported under quasi-static and impact tests, is also present under RCP conditions, and that the HDPE nanocomposites could be used in applications in which RCP must be prevented.

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“…PLA and composite materials are common materials in 4D printing and have been thoroughly investigated [12,13]. For example, grippers were developed that consist of a shape-memory polymer (SMP).…”

Section: Introductionmentioning

confidence: 99%

4D Printing: A Methodical Approach to Product Development Using Smart Materials

Junk,

Einloth,

Velten

2023

Machines

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In 4D printing, an additively manufactured component is given the ability to change its shape or function in an intended and useful manner over time. The technology of 4D printing is still in an early stage of development. Nevertheless, interesting research and initial applications exist in the literature. In this work, a novel methodical approach is presented that helps transfer existing 4D printing research results and knowledge into solving application tasks systematically. Moreover, two different smart materials are analyzed, used, and combined following the presented methodical approach to solving the given task in the form of recovering an object from a poorly accessible space. This is implemented by self-positioning, grabbing, and extracting the target object. The first smart material used to realize these tasks is a shape-memory polymer, while the second is a polymer-based magnetic composite. In addition to the presentation and detailed implementation of the methodical approach, the potentials and behavior of the two smart materials are further examined and narrowed down as a result of the investigation. The results show that the developed methodical approach contributes to moving 4D printing closer toward a viable alternative to existing technologies due to its problem-oriented nature.

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Experimental data-driven uncertainty quantification for the dynamic fracture toughness of particulate polymer composites

Cited by 12 publications

References 54 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

Dynamic Fracture Resistance under Plane Strain Conditions of High-Density Polyethylene Nanoclay Composites

4D Printing: A Methodical Approach to Product Development Using Smart Materials

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