Modeling of the viscoelastic properties of thermoset vinyl ester nanocomposite using artificial neural network

Omari, Mohammad A.; Almagableh, Ahmad; Sevostianov, Igor; Ashhab, Moh’d Sami; Yaseen, Ahmad Bani

doi:10.1016/j.ijengsci.2020.103242

Cited by 29 publications

(8 citation statements)

References 27 publications

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“…However, future saturation in conventional research areas is expected along with the growth of DL applications in areas where DL is currently underrepresented, including physics and various engineering fields [4][5][6][7]. Recent engineering applications include fault detection and material characterization along with DL-assisted analysis techniques [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Towards End-to-End Deep Learning Performance Analysis of Electric Motors

2021

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Convolutional Neural Networks (CNNs) and Deep Learning (DL) revolutionized numerous research fields including robotics, natural language processing, self-driving cars, healthcare, and others. However, DL is still relatively under-researched in physics and engineering. Recent works on DL-assisted analysis showed enormous potential of CNN applications in electrical engineering. This paper explores the possibility of developing an end-to-end DL analysis method to match or even surpass conventional analysis techniques such as finite element analysis (FEA) based on the ability of CNNs to predict the performance characteristics of electric machines. The required depth in CNN architecture is studied by comparing a simplistic CNN with three ResNet architectures. Studied CNNs show over 90% accuracy for an analysis conducted under a minute, whereas a FEA of comparable accuracy required 200 h. It is also shown that training CNNs to predict multidimensional outputs can improve CNN performance. Multidimensional output prediction with data-driven methods is further discussed in context of multiphysics analysis showing potential for developing analysis methods that might surpass FEA capabilities.

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Section: Introductionmentioning

confidence: 99%

Towards End-to-End Deep Learning Performance Analysis of Electric Motors

2021

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“…Analysis and modelling of scale phenomena in micro- and nano-structures is a subject of current interest in Engineering Science. 1 Development of simple and computationally convenient methodologies for design and optimization of modern devices 2–8 and nanocomposites 9–11 has been the main motivation of numerous investigations. Crucial point is to take in due account small-scale effects which are technically significant and cannot be overlooked.…”

Section: Introductionmentioning

confidence: 99%

Stress-driven two-phase integral elasticity for Timoshenko curved beams

Vaccaro

Pinnola

Sciarra

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part N:

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In this research, the size-dependent static behaviour of elastic curved stubby beams is investigated by Timoshenko kinematics. Stress-driven two-phase integral elasticity is adopted to model size effects which soften or stiffen classical local responses. The corresponding governing equations of nonlocal elasticity are established and discussed, non-classical boundary conditions are detected and an effective coordinate-free solution procedure is proposed. The presented mixture approach is elucidated by solving simple curved small-scale beams of current interest in Nanotechnology. The contributed results could be useful for design and optimization of modern sensors and actuators.

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“…In Farajpour, Ghayesh, and Farokhi (2019) , the effects of viscoelasticity as well as geometrical imperfections on the mechanics of carbon nanotubes with large deformations are analysed via a modified nonlocal elasticity model. Finally, in Omari, Almagableh, Sevostianov, Ashhab, and Yaseen (2020) and Rahmanpanah, Mouloodi, Burvill, Gohari, and Davies (2020) , the Authors use an artificial neural network to model the viscoelastic properties of a thermoset vinyl ester nanocomposite and predict the load-displacement curve in a long bone, respectively.…”

Section: Introductionmentioning

confidence: 99%