title: Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements.
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.
authors.affiliation: Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. abdulbasetali@gmail.com.
authors.forename: Abdulbaset
authors.initials: A
authors.lastname: Ali
authors.affiliation: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China. hubing@bit.edu.cn.
authors.forename: Bing
authors.initials: B
authors.lastname: Hu
authors.affiliation: Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. oramahi@uwaterloo.ca.
authors.forename: Omar
authors.initials: O
authors.lastname: Ramahi
keywords: artificial intelligence; crack detection; metamaterial; split-ring resonators; waveguide sensors