Modeling of ultrasonic flaw detection processes in the task of searching and visualizing internal defects in assemblies and structures

Introduction. Inverse problems are a specific type of tasks where the consequences of phenomena are studied to identify their causes. They are widely used in scientific studies, specifically, those dealing with large amounts of experimental data. In the presented paper, inverse problems in mechanical engineering and structural diagnostics are considered. These areas require precise methods to identify internal defects in various materials, which can be critical to ensure the safety and efficiency of technical structures. Despite the many flaw detection methods available, there is a need for innovative developments that can provide higher accuracy and efficiency. This study integrates different scientific methods and technologies. It opens up new perspectives in nondestructive testing for the detection of internal defects in various materials and structures. Its objective is to develop and implement nondestructive testing methods based on a neural network device to improve the accuracy of defect identification, as well as to build a neural network model and evaluate its effectiveness for the refinement of ultrasonic visualization of internal defects in solid materials. In this regard, the task to be solved is to create a reliable tool for accurate visualization of sizes, shapes, location and orientation of internal defects in various materials.Materials and Methods. The technique of determining the geometric parameters of defects in materials through nondestructive testing is used. The approach combining modeling of ultrasonic wave propagation in acoustic medium and artificial neural network technologies is applied. This approach identifies nonlinear relationships between the geometry of defects and the amplitude-frequency and amplitude-time data obtained during signal analysis. Artificial neural networks are a model that can be trained on examples, which provides for an effective solution to problems that are difficult to express in traditional forms. The study uses the finite difference method in the time domain. It is applied to identify and visualize internal defects in materials using ultrasonic nondestructive testing and convolutional generative neural networks.Results. A convolutional neural network has been developed to visualize internal defects using ultrasonic nondestructive testing techniques. This neural network successfully determines the size of defects, their location, shape and orientation with high accuracy and reliability.Discussion and Conclusion. The authors highlight the key influence of defect size on the accuracy of ultrasonic imaging in various scenarios. The validation of the model for three different cases of defects with different mechanical parameters has shown that for successful visualization of defects, the wavelength of the ultrasonic pulse must be ten times smaller than the size of the defect. When analyzing the impact of defect size on the accuracy of the neural network, it is found that the visualization error increases for defects of smaller size. It has also been found that the relative speed of sound in materials has a greater effect on the accuracy of the method than the relative density of the material. Based on the results obtained by the authors, it can be argued that the developed methods and technical solutions are of great importance for future research in the field of flaw detection. They have significant potential for scientific and practical applications.