Probabilistic Delamination Diagnosis of Composite Materials Using a Novel Bayesian Imaging Method
In this paper, a probabilistic delamination location and size detection framework is proposed. The delamination probability image using Lamb wave-based damage detection is constructed using the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is proposed. Following this, the composite coupon fatigue testing setup is introduced and the corresponding lamb wave diagnosis signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as well as their confidence bounds. The damage detection results using the proposed methodology are compared with X-ray images for verification and validation. Finally, some conclusions and future works are drawn based on the proposed study.
Resources
1 resource available
-
STI_9457_Probabilistic delamination diagnosis of composite materials using a novel Bayesian Imaging Method_final.pdf
APPLICATION/DOWNLOAD
Find Related Datasets
Search by Tags
Click any tag below to search for similar datasets
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[
"026:00"
]
|
| contactPoint |
{
"fn": "Abhinav Saxena",
"@type": "vcard:Contact",
"hasEmail": "mailto:abhinav.saxena@nasa.gov"
}
|
| description | In this paper, a probabilistic delamination location and size detection framework is proposed. The delamination probability image using Lamb wave-based damage detection is constructed using the Bayesian updating technique. First, the algorithm for the probabilistic delamination detection framework using Bayesian updating (Bayesian Imaging Method - BIM) is proposed. Following this, the composite coupon fatigue testing setup is introduced and the corresponding lamb wave diagnosis signal is collected and interpreted. Next, the obtained signal features are incorporated in the Bayesian Imaging Method to detect delamination size and location, as well as their confidence bounds. The damage detection results using the proposed methodology are compared with X-ray images for verification and validation. Finally, some conclusions and future works are drawn based on the proposed study. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "STI_9457_Probabilistic delamination diagnosis of composite materials using a novel Bayesian Imaging Method_final.pdf",
"format": "application/download",
"mediaType": "application/download",
"description": "STI_9457_Probabilistic delamination diagnosis of composite materials using a novel Bayesian Imaging Method_final.pdf",
"downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/STI_9457_Probabilistic_delamination_diagnosis_of_composite_materials_using_a_novel_Bayesian_Imaging_Method_final.pdf"
}
]
|
| identifier | DASHLINK_851 |
| issued | 2013-12-12 |
| keyword |
[
"ames",
"dashlink",
"nasa"
]
|
| landingPage | https://c3.nasa.gov/dashlink/resources/851/ |
| modified | 2025-03-31 |
| programCode |
[
"026:029"
]
|
| publisher |
{
"name": "Dashlink",
"@type": "org:Organization"
}
|
| title | Probabilistic Delamination Diagnosis of Composite Materials Using a Novel Bayesian Imaging Method |
