A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures
The vibrational behavior of composite structures has been demonstrated as a useful feature for identifying debonding damage. The precision of the damage localization can be greatly improved by the addition of more measuring points. Therefore, full-field vibration measurements, such as those obtained...
Saved in:
| Main Authors: | Viviana Meruane, Diego Aichele, Rafael Ruiz, Enrique López Droguett |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/1483594 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Deep Convolutional Neural Network-Based Structural Damage Localization and Quantification Using Transmissibility Data
by: Sergio Cofre-Martel, et al.
Published: (2019-01-01) -
Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings
by: David Verstraete, et al.
Published: (2017-01-01) -
Real-Time Structural Damage Assessment Using Artificial Neural Networks and Antiresonant Frequencies
by: V. Meruane, et al.
Published: (2014-01-01) -
Damage Assessment of Low-Velocity Impacted Sandwich Composite Structures Using X-Ray Micro-Computed Tomography
by: Tendai Chipanga, et al.
Published: (2024-01-01) -
Bioinspired Sandwich Structure in Composite Panels
by: Deepak Sampathkumar, et al.
Published: (2023-01-01)