Ultrasonic guided wave estimation of minimum remaining wall thickness using Gaussian process regression. (September 2022)

• Record Type: Journal Article
• Authors: Tabatabaeipour, Morteza; Tzaferis, Konstantinos; McMillan, Ross; Jackson, William; Dobie, Gordon; Edwards, Rachel S.; Trushkevych, Oksana; Gachagan, Anthony
• Is Part Of: Materials & design. Volume 221(2022)
• Publication Date: 2022-09
• Subjects: Automated non-destructive evaluation -- Guided waves -- Shear horizontal waves -- Ultrasonics -- Machine learning -- Gaussian process regression; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2022.110990
• Languages: English
• ISSNs: 0264-1275

Abstract

Graphical abstract: Highlights: Instantaneous phase characteristics of guided waves contain a rich source of information for defect characterisation. Gaussian process regression technique is capable of predicting the real defect depth with a large-scale simulated dataset. Gaussian process regression can successfully estimate the depth of wide defects (≥100% of wavelength) in practice. Abstract: Ultrasonic Guided Waves (UGW) offer the possibility of inspecting a strip across a structure rather than just the point under a traditional bulk wave transducer. This can increase the rate of inspection and enable inspection under obstructions. This paper investigates the instantaneous phase characteristics of the shear horizontal guided waves for various defect depths and widths. The Gaussian process regression is then evaluated for estimating the minimum remaining wall thickness between a pair of transducers. A Gaussian process regression model is built using the fusion of large-scale simulated and low-scale real experimental data. For this purpose, a more precise model of an electromagnetic acoustic transducer is initially built by integrating both electromagnetic and elastic wave fields. Then the simulated data set is built after having been calibrated using a genetic algorithm. The examination of an unseen simulated evaluation data set shows that 96 % of data has an error during thickness gauging of less than 10 per cent of wall thickness. Finally, an experimental testing data set containing three different defects with depths of 3.7, 5.7 and 9.2 mm was examined, resulting in a good depth prediction of large defects with less than 1 mm error for defects wider than one wavelength.