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Effective porosity detection in Laser-based additive manufacturing using shallow learning and Physics-informed pyrometer features.

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Bibliographic Details
Title: Effective porosity detection in Laser-based additive manufacturing using shallow learning and Physics-informed pyrometer features.
Authors: Balaraman, Rajesh Kumar1 (AUTHOR), Jafary-Zadeh, Mehdi2,3 (AUTHOR) zadehmj@a-star.edu.sg, Farbiz, Farzam2 (AUTHOR), Raghavan, Nagarajan1,4 (AUTHOR) nagarajan@sutd.edu.sg
Source: International Journal of Advanced Manufacturing Technology. Jun2026, Vol. 144 Issue 9/10, p7237-7252. 16p.
Subjects: Porosity, Pyrometers, Sampling (Process), Thermal analysis, Supervised learning, Real-time computing, Laser deposition
Abstract: 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 presents a novel, physics-informed framework for in situ porosity classification using shallow learning (SL) models and captured thermal data from a dual-wavelength pyrometer sensor. Unlike deep learning models that require high-resolution large datasets and extensive computational resources, our approach leverages engineered features from multi-orientation (0°, 90°, + 45°, and − 45°) thermal profiles – captured along the laser scan, transverse, and diagonal directions – to characterize melt pool behaviour. We introduce two physics-informed features, melt pool distance (MPD) and aspect ratio of maximum temperature to MPD (ARTM), alongside interpretable statistical feature set. To address the severe class imbalance in defect categories (no-, micro-, and macro- porosity), we apply Synthetic Minority Oversampling (SMOTE) and evaluate model performance using traditional metrics and a novel Classification Deviation Error (CDE) metric proposed to capture minority class misclassification. Our results demonstrate that SL models such as logistic regression achieve high classification accuracy (up to 95%), precision (96%), and recall (95%), and low inference latency, making them suitable for real-time monitoring. The proposed approach offers a computationally efficient and interpretable solution for multi-class porosity detection and paves the pathway for closed-loop quality assurance in LBAM. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract: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 presents a novel, physics-informed framework for in situ porosity classification using shallow learning (SL) models and captured thermal data from a dual-wavelength pyrometer sensor. Unlike deep learning models that require high-resolution large datasets and extensive computational resources, our approach leverages engineered features from multi-orientation (0°, 90°, + 45°, and − 45°) thermal profiles – captured along the laser scan, transverse, and diagonal directions – to characterize melt pool behaviour. We introduce two physics-informed features, melt pool distance (MPD) and aspect ratio of maximum temperature to MPD (ARTM), alongside interpretable statistical feature set. To address the severe class imbalance in defect categories (no-, micro-, and macro- porosity), we apply Synthetic Minority Oversampling (SMOTE) and evaluate model performance using traditional metrics and a novel Classification Deviation Error (CDE) metric proposed to capture minority class misclassification. Our results demonstrate that SL models such as logistic regression achieve high classification accuracy (up to 95%), precision (96%), and recall (95%), and low inference latency, making them suitable for real-time monitoring. The proposed approach offers a computationally efficient and interpretable solution for multi-class porosity detection and paves the pathway for closed-loop quality assurance in LBAM. [ABSTRACT FROM AUTHOR]
ISSN:02683768
DOI:10.1007/s00170-026-18211-5