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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.


Schematic drawing of a waveguide sensor scanning a metallic plate with surface cracks.
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f1-sensors-15-11402: Schematic drawing of a waveguide sensor scanning a metallic plate with surface cracks.

Mentions: The sensor used in this work for scanning metallic surfaces was an open-ended waveguide probe enhanced with an array of split-ring resonator (SRR) cells [6]. The waveguide is operating at the Ku-band of 12–18 GHz and has a cross section of 15.8 mm by 7.9 mm, with a standard flange with dimensions of 33.30 mm by 33.30 mm. The experimental setup operates by scanning a metallic plate containing 0.5 mm surface cracks ranging in depth from 0.5 mm to 2.25 mm, with increments of 0.25 mm. Figure 1 below shows a diagram of a waveguide sensor scanning a metallic plate with cracks. The sensor is placed 0.5 mm standoff distance, with the long dimension of the waveguide being parallel to the cracks.


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

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

Schematic drawing of a waveguide sensor scanning a metallic plate with surface cracks.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-15-11402: Schematic drawing of a waveguide sensor scanning a metallic plate with surface cracks.
Mentions: The sensor used in this work for scanning metallic surfaces was an open-ended waveguide probe enhanced with an array of split-ring resonator (SRR) cells [6]. The waveguide is operating at the Ku-band of 12–18 GHz and has a cross section of 15.8 mm by 7.9 mm, with a standard flange with dimensions of 33.30 mm by 33.30 mm. The experimental setup operates by scanning a metallic plate containing 0.5 mm surface cracks ranging in depth from 0.5 mm to 2.25 mm, with increments of 0.25 mm. Figure 1 below shows a diagram of a waveguide sensor scanning a metallic plate with cracks. The sensor is placed 0.5 mm standoff distance, with the long dimension of the waveguide being parallel to the cracks.

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.