Machine Learning Assisted Characterisation and Simulation of Compressive Damage in Composite Laminates

Composite Structures ◽

10.1016/j.compstruct.2021.114290 ◽

2021 ◽

pp. 114290

Author(s):

Johannes Reiner ◽

Reza Vaziri ◽

Navid Zobeiry

Keyword(s):

Machine Learning ◽

Composite Laminates ◽

Compressive Damage

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Machine Learning Based Predictive Model for AFP-Based Unidirectional Composite Laminates

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2019.2932398 ◽

2020 ◽

Vol 16 (4) ◽

pp. 2315-2324 ◽

Cited By ~ 3

Author(s):

Chathura Wanigasekara ◽

Ebrahim Oromiehie ◽

Akshya Swain ◽

B. Gangadhara Prusty ◽

Sing Kiong Nguang

Keyword(s):

Machine Learning ◽

Predictive Model ◽

Composite Laminates ◽

Unidirectional Composite

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Compressive damage modeling of fiber-reinforced composite laminates using 2D higher-order layer-wise models

Composites Part B Engineering ◽

10.1016/j.compositesb.2021.108753 ◽

2021 ◽

pp. 108753

Author(s):

M.H. Nagaraj ◽

J. Reiner ◽

R. Vaziri ◽

E. Carrera ◽

M. Petrolo

Keyword(s):

Composite Laminates ◽

Higher Order ◽

Fiber Reinforced ◽

Damage Modeling ◽

Fiber Reinforced Composite ◽

Reinforced Composite ◽

Compressive Damage

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Machine Learning for Acoustic Emission Signatures in Composite Laminates

American Society for Composites 2017 ◽

10.12783/asc2017/15265 ◽

2017 ◽

Cited By ~ 1

Author(s):

HONG TAT ◽

JASON WU ◽

MATTHEW PIKE ◽

JOSEPH SCHAEFER ◽

VICTOR PAUL PAUCA ◽

...

Keyword(s):

Machine Learning ◽

Acoustic Emission ◽

Composite Laminates

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A methodology to generate design allowables of composite laminates using machine learning

International Journal of Solids and Structures ◽

10.1016/j.ijsolstr.2021.111095 ◽

2021 ◽

pp. 111095

Author(s):

C. Furtado ◽

L.F. Pereira ◽

R.P. Tavares ◽

M. Salgado ◽

F. Otero ◽

...

Keyword(s):

Machine Learning ◽

Composite Laminates

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Prediction of stiffness degradation based on machine learning: Axial elastic modulus of [0 m /90 n ] s composite laminates

Composites Science and Technology ◽

10.1016/j.compscitech.2021.109186 ◽

2021 ◽

pp. 109186

Author(s):

Mingqing Yuan ◽

Haitao Zhao ◽

Yuehan Xie ◽

Hantao Ren ◽

Li Tian ◽

...

Keyword(s):

Machine Learning ◽

Elastic Modulus ◽

Composite Laminates ◽

Stiffness Degradation

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Stochastic Oblique Impact on Composite Laminates: A Concise Review and Characterization of the Essence of Hybrid Machine Learning Algorithms

Archives of Computational Methods in Engineering ◽

10.1007/s11831-020-09438-w ◽

2020 ◽

Author(s):

T. Mukhopadhyay ◽

S. Naskar ◽

S. Chakraborty ◽

P. K. Karsh ◽

R. Choudhury ◽

...

Keyword(s):

Machine Learning ◽

Composite Laminates ◽

Learning Algorithms ◽

Oblique Impact ◽

Machine Learning Algorithms ◽

Concise Review ◽

Hybrid Machine

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A Methodology to Generate Design Allowables of Composite Laminates Using Machine Learning

10.23967/composites.2021.109 ◽

2021 ◽

Author(s):

C. Furtado ◽

L. Pereira ◽

R. Tavares ◽

M. Salgado ◽

F. Otero ◽

...

Keyword(s):

Machine Learning ◽

Composite Laminates

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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