Balci, Z.Mert, A.2025-05-102025-05-1020251058-975910.1080/10589759.2024.23733182-s2.0-105002907021https://doi.org/10.1080/10589759.2024.2373318In this study, we propose a robust photoacoustic (PA) signal processing framework for a material independent defect detection using empirical mode decomposition (EMD) and machine learning algorithms. First, a database of the PA signals with 960 samples has been obtained from aluminium, iron, plastic and wood materials using a laser, microphone and data acquisition board-based PA apparatus. Second, the EMD based time and time-frequency domain techniques are proposed to extract robust cross-material feature space focusing on laser induced acoustic signal, and the decomposed intrinsic mode (IMF) with 14 extracted features are performed on totally 960 samples PA signals to evaluate k-nearest neighbour (k-NN), decision tree (DT) and support vector machine (SVM) classifiers. Inter- material and cross-material evaluations are performed, and the accuracy rates up to 100% for SVM and 97.77% for k-NN are yielded. © 2024 Informa UK Limited, trading as Taylor & Francis Group.eninfo:eu-repo/semantics/closedAccessDecision TreeEmpirical Mode DecompositionK-Nearest NeighbourNon-Destructive TestingPhotoacousticSupport Vector MachineArticle405Q2Q220442056WOS:001258134700001