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Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements.

Ali A, Hu B, Ramahi O - Sensors (Basel) (2015)

Bottom Line: 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.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.The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. abdulbasetali@gmail.com.

ABSTRACT
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.

No MeSH data available.


(a) Photographs of the front and back views of the split-ring resonator (SRR) array etched on a printed circuit board [6]; (b) Photograph of the sensor.
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f2-sensors-15-11402: (a) Photographs of the front and back views of the split-ring resonator (SRR) array etched on a printed circuit board [6]; (b) Photograph of the sensor.

Mentions: A printed circuit board (PCB) with low loss (Rogers 4003) was used to fabricate the SRRs. Figure 2a displays the front and back views of the PCB patch used at the open end of the waveguide. Figure 2b below shows a photograph of the waveguide sensor.


Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements.

Ali A, Hu B, Ramahi O - Sensors (Basel) (2015)

(a) Photographs of the front and back views of the split-ring resonator (SRR) array etched on a printed circuit board [6]; (b) Photograph of the sensor.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481976&req=5

f2-sensors-15-11402: (a) Photographs of the front and back views of the split-ring resonator (SRR) array etched on a printed circuit board [6]; (b) Photograph of the sensor.
Mentions: A printed circuit board (PCB) with low loss (Rogers 4003) was used to fabricate the SRRs. Figure 2a displays the front and back views of the PCB patch used at the open end of the waveguide. Figure 2b below shows a photograph of the waveguide sensor.

Bottom Line: 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.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.The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. abdulbasetali@gmail.com.

ABSTRACT
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.

No MeSH data available.