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.