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

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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]
Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194640960
AccessLevel: 6
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PubTypeId: academicJournal
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  Data: Effective porosity detection in Laser-based additive manufacturing using shallow learning and Physics-informed pyrometer features.
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  Data: <searchLink fieldCode="DE" term="%22Porosity%22">Porosity</searchLink><br /><searchLink fieldCode="DE" term="%22Pyrometers%22">Pyrometers</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+%28Process%29%22">Sampling (Process)</searchLink><br /><searchLink fieldCode="DE" term="%22Thermal+analysis%22">Thermal analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Laser+deposition%22">Laser deposition</searchLink>
– Name: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00170-026-18211-5
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 7237
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      – SubjectFull: Porosity
        Type: general
      – SubjectFull: Pyrometers
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      – SubjectFull: Sampling (Process)
        Type: general
      – SubjectFull: Thermal analysis
        Type: general
      – SubjectFull: Supervised learning
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      – SubjectFull: Real-time computing
        Type: general
      – SubjectFull: Laser deposition
        Type: general
    Titles:
      – TitleFull: Effective porosity detection in Laser-based additive manufacturing using shallow learning and Physics-informed pyrometer features.
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            NameFull: Balaraman, Rajesh Kumar
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            NameFull: Jafary-Zadeh, Mehdi
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            NameFull: Farbiz, Farzam
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 144
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