###### 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 \(0.63\pm 0.03\). 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. photoacoustic imaging; data augmentation; machine learning; regression models; statistical analysis; ANOVA test; internal steel cracks +
Footnote †: journal: Applied sciences  
Article  
In-Depth Steel Crack Analysis Using Photoacoustic Imaging (PAI) with Machine Learning-Based Image Processing Techniques and Evaluating PAI-Based Internal Steel Crack Feasibility. _[PERSON]_ 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,  
depending upon the specific application requirements, result manipulation, time and cost factors, and other parameters. The oldest known technique for NDT is the visual inspection technique starting with the naked eye, but with the advent of technology, the visual inspection technique is augmented with the power of strong microscopes and high-resolution camera lens setups [4]. Vibration-based methods have also been used in the industry for defect detection; however, they are limited by their low sensitivity and accuracy, especially for small subjects [5]. Electromagnetic (EM) techniques offer a vital contribution in the field of NDT, utilizing techniques like eddy current testing, electromagnetic induction, and the sweep frequency technique [6; 7; 8]. 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]. Though these and other NDT techniques may be applied in a standalone setting, a clever approach to counter each other's limitations and benefit from mutual combined strengths is to combine these approaches together. An example of such a combination is the development of the photoacoustic imaging (PAI) technique. As the name suggests, it mutually benefits from laser, acoustic, and imaging techniques.