Enhancing detection and prediction of random defects in conductive materials using eddy current sensor and RBF networks

Abstract

In the industrial sector, ensuring reliability and robustness is of utmost importance. The research goes beyond conventional non-destructive testing by exploring advanced methods for comprehensive defect detection and imaging. It employs state-of-the-art eddy current testing, enhanced by sensor arrays composed of multiple elements arranged in an updated sequential matrix. This innovative configuration overcomes the challenges of magnetic repulsion between sensor elements, significantly speeding up the testing process while ensuring accuracy through detailed imaging of failure paths. Additionally, this study introduces a novel approach for characterizing random defects in aluminum sheets by utilizing machine learning techniques within the Radial Basis Function (RBF) network. This tool, trained on initial data, enables precise predictions of defect shapes and paths, allowing for accurate identification and dimension estimation later. By integrating defect imaging with predictive modeling, our approach not only advances technology in data security but also reduces effort, time, and costs. The research presents a pioneering methodology for defect identification and prediction, as detailed in the following sections.