Effective Porosity Detection in Laser-Based Additive Manufacturing Using Shallow Learning and Physics-Informed Pyrometer Features

Laser-based additive manufacturing (LBAM) has transformed the production of complex metallic components through precise, layer-by-layer deposition. However, porosity defects can compromise the mechanical integrity of printed parts, necessitating effective real-time monitoring and defect detection methods. This study utilizes dual-wavelength pyrometer data to classify melt pool thermal profiles into no-porosity, micro-porosity, and macro-porosity categories, labelled based on X-ray Computed Tomography (CT) scans. Temperature profiles across four orientations (0°, 90°, + 45°, and − 45°) relative to the laser scanning direction were processed through shallow learning models, enhanced with signal processing and physics-informed features, including melt pool distance (MPD) and aspect ratio of maximum temperature to MPD (ARTM). Our approach achieved classification accuracy (up to 95%), precision (96%), and recall (95%) in defect classification. To address challenges in predicting minority classes, we introduce a classification deviation error (CDE) metric. This work demonstrates that shallow learning models, combined with strategically engineered features, provide an efficient and reliable alternative to computationally expensive deep learning methods for in situ defect detection and quality assurance in LBAM.