Balci, ZekeriyaMert, Ahmet2026-02-082026-02-0820251058-97591477-2671https://doi.org/10.1080/10589759.2024.2373318https://hdl.handle.net/20.500.12885/5850In 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.eninfo:eu-repo/semantics/closedAccessPhotoacousticempirical mode decompositionsupport vector machinek-nearest neighbourdecision treenon-destructive testingArticle10.1080/10589759.2024.237331840520442056WOS:0012581347000012-s2.0-105002907021Q1Q2