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

Abstract

Steel plays an indispensable role in our daily lives, permeating various products ranging from essential commodities and recreational gears to information technology devices and general household items. The meticulous evaluation of steel defects holds paramount importance to ensure the secure and dependable operation of the end products. Photoacoustic imaging (PAI) emerges as a promising modality for structural inspection in the realm of health monitoring applications. This study incorporates PAI experimentation to generate an image dataset and employs machine learning techniques to estimate the length and width of surface cracks. Furthermore, the research delves into the feasibility assessment of employing PAI to investigate internal cracks within a steel sample through a numerical simulation-based study. The study’s findings underscore the efficacy of the PAI in achieving precise surface crack detection, with an acceptable root mean square error (RMSE) of 0.63 ± 0.03. The simulation results undergo statistical analysis techniques, including the analysis of variance (ANOVA) test, to discern disparities between pristine samples and those featuring internal cracks at different locations. The results discern statistically significant distinctions in the simulated acoustic responses for samples with internal cracks of varying sizes at identical/different locations (p < 0.001). These results validate the capability of the proposed technique to differentiate between internal crack sizes and positions, establishing it as a viable method for internal crack detection in steel.