Anti-interference damage localization in composite overwrapped pressure vessels using machine learning and ultrasonic guided waves

In-Depth Steel Crack Analysis Using Photoacoustic Imaging (PAI) with Machine Learning-Based Image Processing Techniques and Evaluating PAI-Based Internal Steel Crack Feasibility

“…As described in the literature [44], the element size should be calculated keeping in consideration the minimum wavelength of the propagating acoustic wave, as expressed in the mathematical relation given in Equation (8). Also, the simulation time step and the maximum frequency are associated with the mathematical relation given in Equation (9). Here, ∆I is the element size and λ min is the minimum wavelength of the propagating wave.…”

“…However, EM techniques have a strict requirement for being applicable only on electrically conducive test materials and require physical contact. NDT diagnostics based on guided wave propagation are popular in the inspection of large testing areas [9], but their application is limited by the requirement of sensor installation, involving cost and accessibility factors. With advances in computational resources and advanced signal processing techniques, machine learning and deep learning techniques provide robust, accurate, and fast tools to classify and predict the size and location parameters of the damages in both image and numeric data for applications in the field of NDT [10].…”