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

Jin, Hanxun; Zhang, Enrui; Espinosa, Horacio D.

doi:10.1115/1.4062966

Cited by 43 publications

(11 citation statements)

References 259 publications

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“…More recently, in response to this computational challenge, researchers have introduced machine learning (ML) techniques into established topology optimization methods, capitalizing on recent advancements in AI-assisted methodologies within the realm of solid mechanics. [18][19][20][21] Various integration approaches have emerged, all with the common objective of speeding up the optimization process. 22,23 These approaches include acceleration of iteration, 24,25 generative design, 26,27 postprocessing, 28 and metamodeling.…”

Section: Motivationmentioning

confidence: 99%

Machine learning in solid mechanics: Application to acoustic metamaterial design

Yago,

Sal‐Anglada,

Roca

et al. 2024

Numerical Meth Engineering

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Machine learning (ML) and Deep learning (DL) are increasingly pivotal in the design of advanced metamaterials, seamlessly integrated with material or topology optimization. Their intrinsic capability to predict and interconnect material properties across vast design spaces, often computationally prohibitive for conventional methods, has led to groundbreaking possibilities. This paper introduces an innovative machine learning approach for the optimization of acoustic metamaterials, focusing on Multiresonant Layered Acoustic Metamaterial (MLAM), designed for targeted noise attenuation at low frequencies (below 1000 Hz). This method leverages ML to create a continuous model of the Representative Volume Element (RVE) effective properties essential for evaluating sound transmission loss (STL), and subsequently used to optimize the overall topology configuration for maximum sound attenuation using a Genetic Algorithm (GA). The significance of this methodology lies in its ability to deliver rapid results without compromising accuracy, significantly reducing the computational overhead of complete topology optimization by several orders of magnitude. To demonstrate the versatility and scalability of this approach, it is extended to a more intricate RVE model, characterized by a higher number of parameters, and is optimized using the same strategy. In addition, to underscore the potential of ML techniques in synergy with traditional topology optimization, a comparative analysis is conducted, comparing the outcomes of the proposed method with those obtained through direct numerical simulation (DNS) of the corresponding full 3D MLAM model. This comparative analysis highlights the transformative potential of this combination, particularly when addressing complex topological challenges with significant computational demands, ushering in a new era of metamaterial and component design.

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Section: Motivationmentioning

confidence: 99%

Machine learning in solid mechanics: Application to acoustic metamaterial design

Yago,

Sal‐Anglada,

Roca

et al. 2024

Numerical Meth Engineering

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“…These metrics subsequently function as the design objectives for the force sensor. The diverse sensor architectures, in turn, provide the design space, and the implementation of an AI-enabled reverse design strategy for the sensor architecture could help accelerate the process of customization [ 146 , 263 , 264 , 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 , 273 , 274 , 275 , 276 , 277 , 278 , 279 , 280 ].…”

Section: Summary and Perspectivesmentioning

confidence: 99%

Rational Design of Flexible Mechanical Force Sensors for Healthcare and Diagnosis

Zhang,

Zhang

2023

Materials

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Over the past decade, there has been a significant surge in interest in flexible mechanical force sensing devices and systems. Tremendous efforts have been devoted to the development of flexible mechanical force sensors for daily healthcare and medical diagnosis, driven by the increasing demand for wearable/portable devices in long-term healthcare and precision medicine. In this review, we summarize recent advances in diverse categories of flexible mechanical force sensors, covering piezoresistive, capacitive, piezoelectric, triboelectric, magnetoelastic, and other force sensors. This review focuses on their working principles, design strategies and applications in healthcare and diagnosis, with an emphasis on the interplay among the sensor architecture, performance, and application scenario. Finally, we provide perspectives on the remaining challenges and opportunities in this field, with particular discussions on problem-driven force sensor designs, as well as developments of novel sensor architectures and intelligent mechanical force sensing systems.

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“…This enables a deeper understanding of the fracture behavior of the material. Various numerical methods are employed to solve fracture problems (Buehler 2024;Dankowicz and Chiu 2023;Jin et al 2023), such as singular element (Wang et al 2023), cohesive zone (Zhang and Luo 2022), extended finite element (Tan et al 2022b), and phase field (Gao et al 2022;Miehe et al 2010a;Wilson et al 2013) methods. Among these, the phase field method has proven to be particularly effective.…”

Section: Introductionmentioning

confidence: 99%

Phase field fracture modelling of flexible piezoelectric materials considering different electrical boundary conditions

Lv,

Li,

Shi

et al. 2024

Preprint

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Flexible piezoelectric materials have gained considerable attention due to their remarkable properties, including electromechanical coupling and high stretchability. These materials have found extensive applications in the field of flexible electronic devices. However, the issue of fracture in flexible piezoelectrics cannot be ignored. In general, these flexible/stretchable materials experience fractures when subjected to significant deformation. While previous studies have primarily focused on fracture problems of brittle piezoelectric materials with low failure strain. There is a need to investigate the fracture behavior of flexible piezoelectrics with finite deformation. Within the framework of the phase field method, this work addresses the fracture of flexible piezoelectrics utilizing a nonlinear electromechanical material model. To solve the coupled governing equations, a residual controlled staggered algorithm (RCSA) is employed in the user element subroutine of commercial software ABAQUS. By utilizing the phase field method and a nonlinear electromechanical material model, this study provides insights into the fracture mechanisms and the effects of various factors on the fracture behavior of these materials. Specifically, the effects of external electric fields, displacements, and various electrical boundary conditions across the crack are investigated. This research contributes to a better understanding of flexible piezoelectric materials and can aid in the development of strategies to enhance their fracture resistance and durability in practical applications.

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

Cited by 43 publications

References 259 publications

Machine learning in solid mechanics: Application to acoustic metamaterial design

Machine learning in solid mechanics: Application to acoustic metamaterial design

Rational Design of Flexible Mechanical Force Sensors for Healthcare and Diagnosis

Phase field fracture modelling of flexible piezoelectric materials considering different electrical boundary conditions

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