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<p>One of the major challenges in powder bed fusion using a laser beam for metals (PBF-LB/M) additive manufacturing is the presence of sub-surface defects and porosities, which significantly reduce the fatigue performance of produced parts. Although various in situ monitoring strategies have been proposed, the accurate and early detection of porosity defects during actual manufacturing remains a critical challenge. Existing monitoring methods often suffer from high costs, limited precision, or poor industrial applicability. This study proposes an integrated, cost-effective approach for real-time in situ quality control of PBF-LB/M processes to detect defects in each layer. The analysis investigates how acoustic-ultrasonic signal signatures reflect process variations associated with the formation of porosity defects in overhang zones. Furthermore, by comparing several machine learning approaches, this study provides a comprehensive evaluation of the relative performance of different algorithms. The comparative analysis demonstrates that the artificial neural network achieves the best performance, with a success rate of 92% in both accuracy and F1-score, while also highlighting the method’s scalability and practical applicability for PBF-LB/M process monitoring. The findings confirm the effectiveness of the layer-by-layer monitoring framework based on acoustic-ultrasonic sensing and machine learning, offering a practical and scalable solution for improving defect detection and overall part quality in industrial PBF-LB/M processes.</p>

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Combination of acoustic-ultrasonic monitoring and machine learning for layer-by-layer defect detection in PBF-LB/M

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • El Arbi Hajjioui,
  • Foued Abroug,
  • Khanh Nguyen,
  • Maher Baili,
  • Kamal Medjaher,
  • Lionel Arnaud

摘要

One of the major challenges in powder bed fusion using a laser beam for metals (PBF-LB/M) additive manufacturing is the presence of sub-surface defects and porosities, which significantly reduce the fatigue performance of produced parts. Although various in situ monitoring strategies have been proposed, the accurate and early detection of porosity defects during actual manufacturing remains a critical challenge. Existing monitoring methods often suffer from high costs, limited precision, or poor industrial applicability. This study proposes an integrated, cost-effective approach for real-time in situ quality control of PBF-LB/M processes to detect defects in each layer. The analysis investigates how acoustic-ultrasonic signal signatures reflect process variations associated with the formation of porosity defects in overhang zones. Furthermore, by comparing several machine learning approaches, this study provides a comprehensive evaluation of the relative performance of different algorithms. The comparative analysis demonstrates that the artificial neural network achieves the best performance, with a success rate of 92% in both accuracy and F1-score, while also highlighting the method’s scalability and practical applicability for PBF-LB/M process monitoring. The findings confirm the effectiveness of the layer-by-layer monitoring framework based on acoustic-ultrasonic sensing and machine learning, offering a practical and scalable solution for improving defect detection and overall part quality in industrial PBF-LB/M processes.