Anomaly Detection in Directed Energy Deposition: A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms

Berke Ayyıldızlı, Beyza Balota, Kerem Tatari, Shawqi Mohammed Farea, Mustafa Unel

2025

Abstract

Directed Energy Deposition (DED) is a promising additive manufacturing technology increasingly utilized in critical industries such as aerospace and biomedical engineering for fabricating complex metal components. However, ensuring the structural integrity of DED-fabricated parts remains a significant challenge due to the emergence of in-process defects. To address this, we propose a comprehensive anomaly detection framework that leverages in-situ thermal imaging of the melt pool for defect identification. Our approach encompasses both supervised and unsupervised machine learning techniques to capture diverse defect patterns and accommodate varying levels of labeled data availability. Supervised methods-including ensemble classifiers and deep neural networks-are employed to learn from annotated thermal data, while unsupervised methods, such as autoencoders and clustering algorithms, are used to detect anomalies in unlabeled scenarios and uncover previously unknown defect patterns. The pipeline incorporates essential preprocessing techniques-such as feature extraction, normalization, and class rebalancing-to enhance model robustness. Experimental evaluations offer a detailed comparison between the supervised classifiers and unsupervised models utilized in this work, emphasizing the predictive performance and practical applicability of each learning paradigm. Notably, the supervised classification-based framework achieved high performance in detecting porosity-related anomalies, with an F1 score of up to 0.88 and accuracy reaching 99%.

Paper Citation

in Harvard Style

Ayyıldızlı B., Balota B., Tatari K., Farea S. and Unel M. (2025). Anomaly Detection in Directed Energy Deposition: A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 503-510. DOI: 10.5220/0013729800003982

in Bibtex Style

@conference{icinco25,
author={Berke Ayyıldızlı and Beyza Balota and Kerem Tatari and Shawqi Mohammed Farea and Mustafa Unel},
title={Anomaly Detection in Directed Energy Deposition: A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={503-510},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013729800003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - Anomaly Detection in Directed Energy Deposition: A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms
SN - 978-989-758-770-2
AU - Ayyıldızlı B.
AU - Balota B.
AU - Tatari K.
AU - Farea S.
AU - Unel M.
PY - 2025
SP - 503
EP - 510
DO - 10.5220/0013729800003982
PB - SciTePress