Ultrasonic wave field image augmentation in PZT sensors using generative machine learning and Coulomb coupling

This paper presents an approach to overcome the time-intensive nature of the Coulomb coupling imaging method by employing Generative Adversarial Networks (GANs) for data augmentation. Coulomb coupling, an experimental technique, is essential for visualizing ultrasonic wave propagation in piezoelectric materials and is valuable in various domains including materials research. It provides valuable insights such as finding mechanical properties and detecting anomalies in piezoelectric materials. However, the efficiency of this method is hindered by traditional time expansive point-by-point scanning. Integrating advanced machine learning into Coulomb coupling imaging has emerged as a promising solution to address this issue. Nonetheless, the lack of sufficient data has been a significant challenge. The key contribution is the use of GANs to create synthetic yet realistic images from a limited set of real data, effectively overcoming the issue of data scarcity. A large number of artificial images were successfully generated, expediting model training and enhancing generalization. This study is the first to use GANs in Coulomb coupling imaging, showing its transformative potential. By overcoming data limitations, the proposed approach enhances Coulomb coupling imaging and enables its integration with advanced technologies like AI-driven predictive modeling and real-time adaptive imaging. This opens new frontiers for applications in materials science and other imaging modalities.