Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements
This work presents a real life experiment of implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human obs...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/15/5/11402 |
| _version_ | 1811299694319501312 |
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| author | Abdulbaset Ali Bing Hu Omar Ramahi |
| author_facet | Abdulbaset Ali Bing Hu Omar Ramahi |
| author_sort | Abdulbaset Ali |
| collection | DOAJ |
| description | This work presents a real life experiment of implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor’s signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impact in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates. |
| first_indexed | 2024-04-13T06:39:37Z |
| format | Article |
| id | doaj.art-c7b3b05e2dc946e5888eb3cbfad4b09e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:39:37Z |
| publishDate | 2015-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c7b3b05e2dc946e5888eb3cbfad4b09e2022-12-22T02:57:46ZengMDPI AGSensors1424-82202015-05-01155114021141610.3390/s150511402s150511402Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial ElementsAbdulbaset Ali0Bing Hu1Omar Ramahi2Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, CanadaSchool of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaDepartment of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, CanadaThis work presents a real life experiment of implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor’s signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impact in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates.http://www.mdpi.com/1424-8220/15/5/11402artificial intelligencesplit-ring resonatorsmetamaterialcrack detectionwaveguide sensors |
| spellingShingle | Abdulbaset Ali Bing Hu Omar Ramahi Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements Sensors artificial intelligence split-ring resonators metamaterial crack detection waveguide sensors |
| title | Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements |
| title_full | Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements |
| title_fullStr | Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements |
| title_full_unstemmed | Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements |
| title_short | Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements |
| title_sort | intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements |
| topic | artificial intelligence split-ring resonators metamaterial crack detection waveguide sensors |
| url | http://www.mdpi.com/1424-8220/15/5/11402 |
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