Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework

Vigneashwara Pandiyan (), Di Cui, Roland Axel Richter, Annapaola Parrilli and Marc Leparoux
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Vigneashwara Pandiyan: Swiss Federal Laboratories for Materials Science and Technology (Empa)
Di Cui: Swiss Federal Laboratories for Materials Science and Technology (Empa)
Roland Axel Richter: Swiss Federal Laboratories for Materials Science and Technology (Empa)
Annapaola Parrilli: Swiss Federal Laboratories for Materials Science and Technology (EMPA)
Marc Leparoux: Swiss Federal Laboratories for Materials Science and Technology (Empa)

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 2, No 8, 909-933

Abstract: Abstract Artificial Intelligence (AI) has emerged as a promising solution for real-time monitoring of the quality of additively manufactured (AM) metallic parts. This study focuses on the Laser-based Directed Energy Deposition (L-DED) process and utilizes embedded vision systems to capture critical melt pool characteristics for continuous monitoring. Two self-learning frameworks based on Convolutional Neural Networks and Transformer architecture are applied to process zone images from different DED process regimes, enabling in-situ monitoring without ground truth information. The evaluation is based on a dataset of process zone images obtained during the deposition of titanium powder (Cp-Ti, grade 1), forming a cube geometry using four laser regimes. By training and evaluating the Deep Learning (DL) algorithms using a co-axially mounted Charged Couple Device (CCD) camera within the process zone, the down-sampled representations of process zone images are effectively used with conventional classifiers for L-DED process monitoring. The high classification accuracies achieved validate the feasibility and efficacy of self-learning strategies in real-time quality assessment of AM. This study highlights the potential of AI-based monitoring systems and self-learning algorithms in quantifying the quality of AM metallic parts during fabrication. The integration of embedded vision systems and self-learning algorithms presents a novel contribution, particularly in the context of the L-DED process. The findings open avenues for further research and development in AM process monitoring, emphasizing the importance of self-supervised in situ monitoring techniques in ensuring part quality during fabrication.

Keywords: Directed energy deposition; Process monitoring; Self-supervised learning; Melt pool; Convolutional Neural Network (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02279-x

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