A model-based deep learning framework for damage classification and detection in polycarbonate infused with AEROSIL under dynamic loading conditions-Reference-Cited by-同舟云学术

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A model-based deep learning framework for damage classification and detection in polycarbonate infused with AEROSIL under dynamic loading conditions

Published:2024-12 Issue: Volume:287 Page:111810
ISSN:1359-8368
Container-title:Composites Part B: Engineering
language:en
Short-container-title:Composites Part B: Engineering

Author:

Qarssis Youssef^ORCID,Karine Ayoub,Sayed Sirine,Daly Mohamed^ORCID,Nachtane Mourad^ORCID,Tarfaoui Mostapha^ORCID

Publisher

Elsevier BV

Reference43 articles.

1. An investigation of in-plane dynamic behavior of adhesively bonded composite joints under dynamic compression at high strain rate;Sassi;Compos Struct,2018

2. Experimental investigation on the dynamic behavior of 3D printed CF-PEKK composite under cyclic uniaxial compression;Nachtane;Compos Struct,2020

3. A practical note for SHPB test with new algorithms for delimiting pulses;Larbi;Compos Struct,2015

4. Energy dissipation of unstitched and stitched woven composite materials during dynamic compression test;Tarfaoui;Compos B Eng,2019

5. A progressive damage modelling of glass/epoxy cylindrical structure subjected to low-velocity impact;Mohammed;Eng Fail Anal,2022

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