Automated Defect Sizing in FDM PETG Components: A Comparative Study of Segmentation Techniques

While Fused Deposition Modelling (FDM) printing parameters for Polyethylene terephthalate glycol (PETG) are extensively researched, the non-destructive testing (NDT) of its internal defects remains scarce. This study presents an automated defect characterization framework using Step Heating thermography. To overcome the 1D limitations of dynamic Full Width at Half Maximum (FWHM) regression in identifying deep or complex defects, full 2D morphological reconstruction is required. Therefore, Principal Component Analysis (PCA) is applied to extract high-contrast thermal maps, enabling a comparative evaluation of classical segmentation techniques (Edge Detection, Mexican Hat, Otsu) against Deep Learning (YOLOv8). This work aims to provide a solid foundation for standardizing automated quality control in additive manufacturing.