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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.<\/jats:p>","DOI":"10.1007\/s10845-023-02279-x","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T15:02:48Z","timestamp":1703170968000},"page":"909-933","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6043-5696","authenticated-orcid":false,"given":"Vigneashwara","family":"Pandiyan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roland Axel","family":"Richter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annapaola","family":"Parrilli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Leparoux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"2279_CR1","doi-asserted-by":"publisher","unstructured":"Abe, N., Tanigawa, D., Tsukamoto, M., Hayashi, Y., Yamazaki, H., Tatsumi, Y., & Yoneyama, M. 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