Deep Learning for Time Series-Based Acoustic Emission Damage Classification in Composite Materials

[1]  M. V. Kini,et al.  Fatigue behaviour and damage characterization of quasi-isotropic carbon/epoxy laminates , 2022, Cogent Engineering.

[2]  Y. Liu,et al.  Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials , 2022, Materials.

[3]  A. Hall,et al.  The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review , 2022, Journal of Composite Materials.

[4]  Shirsendu Sikdar,et al.  Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel , 2022, Composites Part B: Engineering.

[5]  Tae-Min Oh,et al.  Detection of source locations in RC columns using machine learning with acoustic emission data , 2021 .

[6]  M. Yildiz,et al.  An experimental investigation on damage mechanisms of thick hybrid composite structures under flexural loading using multi-instrument measurements , 2021 .

[7]  T. Pollock,et al.  Damage mechanism identification in composites via machine learning and acoustic emission , 2021, npj Computational Materials.

[8]  Roger Quispe-Dominguez,et al.  Rotational molding parameters of wood-plastic composite materials made of recycled high density polyethylene and wood particles , 2021, Composites Part B: Engineering.

[9]  S. Salamone,et al.  A Deep Learning Framework for Acoustic Emission Sources Localization and Characterization in Complex Aerospace Panels , 2021 .

[10]  G. Rajan,et al.  Fibre Bragg Grating Based Acoustic Emission Measurement System for Structural Health Monitoring Applications , 2021, Materials.

[11]  Puhui Chen,et al.  Theoretical analysis and experimental investigation of the occurrence of fiber bridging in unidirectional laminates under Mode I loading , 2021, Composite Structures.

[12]  Yongjun Wang,et al.  On buckling behaviors of a typical bending-dominated periodic lattice , 2020 .

[13]  Craig M. Hamel,et al.  Modeling the dissolution of thermosetting polymers and composites via solvent assisted exchange reactions , 2020 .

[14]  D. B. Hibbert,et al.  Specific risks of false decisions in conformity assessment of a substance or material with a mass balance constraint – A case study of potassium iodate , 2020, Measurement.

[15]  Christopher Hsu,et al.  Deep machine learning for detection of acoustic wave reflections , 2020, Structural Health Monitoring.

[16]  N. Petrinic,et al.  Multi-material adhesively bonded structures: Characterisation and modelling of their rate-dependent performance , 2020 .

[17]  Giorgio Visani,et al.  Metrics for Multi-Class Classification: an Overview , 2020, ArXiv.

[18]  Nader Ale Ebrahim,et al.  Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning , 2020, IEEE Access.

[19]  T. Niendorf,et al.  A micromechanical-based finite element simulation of process-induced residual stresses in metal-CFRP-hybrid structures , 2020 .

[20]  Thomas G. Dietterich.  What is machine learning? , 2015, Archives of Disease in Childhood.

[21]  Seokpum Kim,et al.  Factors affecting direct lightning strike damage to fiber reinforced composites: A review , 2020 .

[22]  L. Ye,et al.  Identification of damage mechanisms of carbon fiber reinforced silicon carbide composites under static loading using acoustic emission monitoring , 2019, Ceramics International.

[23]  Suresh Kumar Chelliah,et al.  Characterization of failure mechanism in glass, carbon and their hybrid composite laminates in epoxy resin by acoustic emission monitoring , 2019, Nondestructive Testing and Evaluation.

[24]  J. Myers,et al.  A novel and effective anchorage system for enhancing the flexural capacity of RC beams strengthened with FRCM composites , 2019, Composite Structures.

[25]  Heng Liu,et al.  Image-driven structural steel damage condition assessment method using deep learning algorithm , 2019, Measurement.

[26]  Kaifu Zhang,et al.  Multi-stage mechanical behavior and failure mechanism analysis of CFRP/Al single-lap bolted joints with different seawater ageing conditions , 2019, Composite Structures.

[27]  Jong-Ho Kim,et al.  Enhanced electromechanical performance of P(VDF-TrFE-CTFE) thin films hybridized with highly dispersed carbon blacks , 2018, Composites Part B: Engineering.

[28]  Feng Liu,et al.  Interfacial behavior and debonding failures of full-scale CFRP-strengthened H-section steel beams , 2018, Composite Structures.

[29]  Germain Forestier,et al.  Deep learning for time series classification: a review , 2018, Data Mining and Knowledge Discovery.

[30]  S. Hloch,et al.  Acoustic emission for interlaminar toughness testing of CFRP: Evaluation of the crack growth due to burst analysis , 2018 .

[31]  Hwa Kian Chai,et al.  Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique , 2016 .

[32]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[33]  Pedro P. Camanho,et al.  A damage model for the simulation of delamination in advanced composites under variable-mode loading , 2006 .

[34]  Lorenzo Iannucci,et al.  Fracture toughness of the tensile and compressive fibre failure modes in laminated composites , 2006 .

[35]  F. Chiang,et al.  Full-field experimental analysis of a sandwich beam under bending and comparison with theories , 2021 .

[36]  D. Peterson,et al.  Deep learning based characterization of nanoindentation induced acoustic events , 2021 .

[37]  P. Sevvel,et al.  Acoustic emission based deep learning technique to predict adhesive bond strength of laser processed CFRP composites , 2020 .

[38]  Rukshan Navaratne,et al.  A Generic Framework for Application of Machine Learning in Acoustic Emission-Based Damage Identification , 2019, Lecture Notes in Mechanical Engineering.

[39]  K. Chawla.  Composite Materials: Science and Engineering , 2019 .

[40]  Nikhil Ketkar,et al.  Deep Learning with Python , 2017 .