Semi-Supervised Anomaly Detection in Directed Energy Deposition Using Thermal Images

Ufuk Ismail Ozdek, Yigit Kaan Tonkaz, Shawqi Mohammed Farea, Mustafa Unel

2025

Abstract

Directed Energy Deposition (DED) is a crucial additive manufacturing process used in aerospace and healthcare applications, among others. However, ensuring defect-free production remains a challenge due to the difficulty in detecting defect-related anomalies in real-time. In this study, we address the problem of defect detection in DED processes through thermal images of melt pools. As an anomaly detection problem, we adopt a semi-supervised approach based on One-Class Support Vector Machine (OCSVM) and Isolation Forest (iForest). We analyze the performance of these models across different feature sets. Additionally, this semi-supervised approach is compared against an unsupervised approach utilizing the same learning algorithms. The results indicate the superiority of the semi-supervised approach for both algorithms. Yet, iForest outperforms OCSVM with an accuracy of 95% and an F1-score of 0.88, demonstrating its robustness in distinguishing defective from non-defective instances. This work provides valuable insights into the applicability of semi-supervised machine learning techniques for real-time defect detection in DED processes. By leveraging thermal imaging data and feature-based anomaly detection models, our findings contribute to the development of efficient, non-destructive quality control mechanisms for additive manufacturing processes.

Paper Citation

in Harvard Style

Ozdek U., Tonkaz Y., Farea S. and Unel M. (2025). Semi-Supervised Anomaly Detection in Directed Energy Deposition Using Thermal Images. 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 437-445. DOI: 10.5220/0013729700003982

in Bibtex Style

@conference{icinco25,
author={Ufuk Ismail Ozdek and Yigit Kaan Tonkaz and Shawqi Mohammed Farea and Mustafa Unel},
title={Semi-Supervised Anomaly Detection in Directed Energy Deposition Using Thermal Images},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2025},
pages={437-445},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013729700003982},
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 - Semi-Supervised Anomaly Detection in Directed Energy Deposition Using Thermal Images
SN - 978-989-758-770-2
AU - Ozdek U.
AU - Tonkaz Y.
AU - Farea S.
AU - Unel M.
PY - 2025
SP - 437
EP - 445
DO - 10.5220/0013729700003982
PB - SciTePress