High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation Algorithm Article Swipe
Mengyuan Ma
,
Hongyi Cao
,
Mingshun Jiang
,
Lin Sun
,
Lei Zhang
,
Faye Zhang
,
Qingmei Sui
,
Aiqin Tian
,
Jianying Liang
,
Lei Jia
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/ma13173840
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/ma13173840
This paper presents a method based on signal correlation to detect delamination defects of widely used carbon fiber reinforced plastic with high precision and a convenient process. The objective of it consists in distinguishing defect and non-defect signals and presenting the depth and size of defects by image. A necessary reference signal is generated from the non-defect area by using autocorrelation theory firstly. Through the correlation calculation results, the defect signal and non-defect signal are distinguished by using Euclidean distance. In order to get more accurate time-of-flight, cubic spline interpolation is introduced. In practical automatic ultrasonic A-scan signal processing, signal correlation provide a new way to avoid problems such as signal peak tracking and complex gate setting. Finally, the detection results of a carbon fiber laminate with artificial delamination through ultrasonic phased array C-scan acquired from Olympus OmniScan MX2 and this proposed algorithm are compared, which showing that this proposed algorithm performs well in defect shape presentation and location calculation. The experiment shows that the defect size error is less than 4%, the depth error less than 3%. Compared with ultrasonic C-scan method, this proposed method needs less inspector’s prior-knowledge, which can lead to advantages in automatic ultrasonic testing.
Related Topics
Concepts
Ultrasonic sensor
SIGNAL (programming language)
Algorithm
Autocorrelation
Delamination (geology)
Materials science
Signal processing
Acoustics
Interpolation (computer graphics)
Ultrasonic testing
Computer science
Artificial intelligence
Mathematics
Digital signal processing
Physics
Statistics
Motion (physics)
Biology
Subduction
Programming language
Paleontology
Tectonics
Computer hardware
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ma13173840
- https://www.mdpi.com/1996-1944/13/17/3840/pdf?version=1598946770
- OA Status
- gold
- Cited By
- 30
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3082048883
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3082048883Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ma13173840Digital Object Identifier
- Title
-
High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-31Full publication date if available
- Authors
-
Mengyuan Ma, Hongyi Cao, Mingshun Jiang, Lin Sun, Lei Zhang, Faye Zhang, Qingmei Sui, Aiqin Tian, Jianying Liang, Lei JiaList of authors in order
- Landing page
-
https://doi.org/10.3390/ma13173840Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1944/13/17/3840/pdf?version=1598946770Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1996-1944/13/17/3840/pdf?version=1598946770Direct OA link when available
- Concepts
-
Ultrasonic sensor, SIGNAL (programming language), Algorithm, Autocorrelation, Delamination (geology), Materials science, Signal processing, Acoustics, Interpolation (computer graphics), Ultrasonic testing, Computer science, Artificial intelligence, Mathematics, Digital signal processing, Physics, Statistics, Motion (physics), Biology, Subduction, Programming language, Paleontology, Tectonics, Computer hardwareTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 12, 2023: 5, 2022: 7, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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