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<p>Laser powder bed fusion (L-PBF) is highly sensitive to process instabilities, making reliable in-situ anomaly detection essential for quality assurance. In this study, we propose a synergistic framework that integrates image preprocessing, CAD-guided region segmentation, and lightweight convolutional neural networks (CNNs) for layer-wise anomaly detection using CCD images acquired during L-PBF. Process-specific image preprocessing techniques, including histogram equalization and contrast enhancement, are employed to improve defect visibility under low-contrast conditions. Furthermore, CAD-guided segmentation is used to explicitly separate part and powder regions, enabling region-specific classification of anomalies such as super-elevation and recoater streaking. Lightweight CNN models are then trained on segmented regions to achieve robust performance with reduced computational cost. Experimental results obtained from twelve datasets demonstrate that the proposed framework significantly improves detection accuracy compared to raw-image-based learning, with accuracy increasing from below 23% to over 80% in representative cases, while maintaining stable performance across different anomaly types. The results confirm that combining process-aware preprocessing with region-specific learning effectively enhances anomaly discrimination and supports practical, low-compute deployment for real-time monitoring in industrial L-PBF systems.</p>

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Development of a synergistic framework combining preprocessing and CAD-guided segmentation with lightweight CNNs for L-PBF anomaly detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Do Young Jeoung,
  • Min Seok Jang,
  • Yeong gyeong Kim,
  • Simo Yeon,
  • Yong Son,
  • Kyunsuk Choi,
  • Hyub Lee,
  • Young Won Kim

摘要

Laser powder bed fusion (L-PBF) is highly sensitive to process instabilities, making reliable in-situ anomaly detection essential for quality assurance. In this study, we propose a synergistic framework that integrates image preprocessing, CAD-guided region segmentation, and lightweight convolutional neural networks (CNNs) for layer-wise anomaly detection using CCD images acquired during L-PBF. Process-specific image preprocessing techniques, including histogram equalization and contrast enhancement, are employed to improve defect visibility under low-contrast conditions. Furthermore, CAD-guided segmentation is used to explicitly separate part and powder regions, enabling region-specific classification of anomalies such as super-elevation and recoater streaking. Lightweight CNN models are then trained on segmented regions to achieve robust performance with reduced computational cost. Experimental results obtained from twelve datasets demonstrate that the proposed framework significantly improves detection accuracy compared to raw-image-based learning, with accuracy increasing from below 23% to over 80% in representative cases, while maintaining stable performance across different anomaly types. The results confirm that combining process-aware preprocessing with region-specific learning effectively enhances anomaly discrimination and supports practical, low-compute deployment for real-time monitoring in industrial L-PBF systems.