THzResNet: A Physics-Inspired Two-Stream Residual Network for Thermal Barrier Coating Thickness Measurement

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 7; pp. 8328 - 8339
• Main Authors: Sun, Fengshan, Fan, Mengbao, Cao, Binghua, Liu, Lin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical model; Analytical models; Coatings; Informatics; Microstructure; Physics; Refractive index; Refractivity; residual network; Surface roughness; Terahertz nondestructive testing (THzResNet); Thermal barrier coatings; thermal barrier coatings (TBCs); Thickness measurement; Training
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3217829

The uneven and complicated porose microstructure of thermal barrier coatings brings a big challenge for accurate and reliable thickness measurement due to the resultant change of refractive index. In this article, a novel insight into the physics, the first two peaks are originally adopted as the preferred signal, and a physics-inspired two-stream deep learning framework, termed as the terahertz (THz) residual network (THzResNet) is proposed to accurately infer topcoat thickness by extracting refractive index online. Specifically, the division and reciprocal layers are figured out and incorporated into the first-stream, yielding the reciprocal of refractive index. Then, a product layer connects it with time-of-flight from the second-stream to generate topcoat thickness, which endows the THzResNet in accord with the THz physics. Finally, the experiments demonstrate the advantages of THzResNet in accuracy and efficiency. The presented approach allows for addressing the challenge for accurate thickness measurement of coatings with complicated microstructures.