Machine Learning for Camera-Based Monitoring of Laser Welding Processes

Aug 3, 2026

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The thesis focuses on applying machine learning to camera-based monitoring of laser welding processes. It frames the problem within industrial information technology and emphasizes data-driven perception as a foundation for observing welding behavior. The work includes academic attribution to the Institute of Industrial Information Technology at Karlsruhe Institute of Technology and acknowledges industrial collaboration during the doctoral research. It is presented as a doctoral dissertation with formal examination details and supervisory guidance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages258

Reading Time10 h 50 min

AccessFree to download

Forschungsberichte aus der Industriellen Informationstechnik 32 Julia Hartung Machine Learning for Camera-Based Monitoring of Laser Welding Processes Julia Hartung Machine Learning for Camera-Based Monitoring of Laser Welding Processes Forschungsberichte aus der Industriellen Informationstechnik Band 32 Institut für Industrielle Informationstechnik Karlsruher Institut für Technologie Hrsg. Prof. Dr.-Ing. Michael Heizmann Eine Übersicht aller bisher in dieser Schriftenreihe erschienenen Bände finden Sie am Ende des Buchs . Machine Learning for Camera-Based Monitoring of Laser Welding Processes by Julia Hartung Karlsruher Institut für Technologie Institut für Industrielle Informationstechnik Machine Learning for Camera-Based Monitoring of Laser Welding Processes Zur Erlangung des akademischen Grades einer Doktorin der Ingenieurswissenschaften von der KIT-Fakultät für Elektrotechnik und Informationstechnik des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation von Julia Hartung, M.Sc. Tag der mündlichen Prüfung: 20. September 2023 Hauptreferent: Prof. Dr.-Ing. Michael Heizmann, KIT Korreferent: Prof. Dr.-Ing. Thomas Längle, KIT Impressum Karlsruher Institut für Technologie (KIT) KIT Scientific Publishing Straße am Forum 2 D-76131 Karlsruhe KIT Scientific Publishing is a registered trademark of Karlsruhe Institute of Technology. Reprint using the book cover is not allowed. [www.ksp.kit.edu](www.ksp.kit.edu) This document – excluding parts marked otherwise, the cover, pictures and graphs – is licensed under a Creative Commons Attribution-Share Alike 4.0 International License (CC BY-SA 4.0): [https://creativecommons.org/licenses/by-sa/4.0/deed.en](https://creativecommons.org/licenses/by-sa/4.0/deed.en) The cover page is licensed under a Creative Commons Attribution-No Derivatives 4.0 International License (CC BY-ND 4.0): [https://creativecommons.org/licenses/by-nd/4.0/deed.en](https://creativecommons.org/licenses/by-nd/4.0/deed.en) Print on Demand 2024 – Gedruckt auf FSC-zertifiziertem Papier ISSN 2190-6629 ISBN 978-3-7315-1333-9 DOI 10. 5445/KSP/1000164716 Preface I want to express my sincere gratitude to all those who contributed to completing this doctoral thesis during my work at Trumpf Laser GmbH in Schramberg in collaboration with the Institute of Industrial Information Technology of the Karlsruhe Institute of Technology. First and foremost, I extend my deepest appreciation to my supervisor, Prof. Dr. Michael Heizmann. His guidance, unwavering support, and invaluable insights have been instrumental in shaping this research and my academic journey. I am equally grateful to Dr. Andreas Jahn for his exceptional mentorship. His stimulating discussions, brilliant ideas, and consistent encouragement have played a pivotal role in the development of this thesis. I extend my thanks to all my colleagues at TRUMPF Laser GmbH, who have contributed to this thesis through their insightful discussions, shared knowledge, and collaborative spirit. I am also grateful to the students who supported my thesis with their dedicated work. Furthermore, I thank Prof. Dr. Thomas Längle for serving as the coreferent for this thesis. On a personal note, I extend my heartfelt thanks to my family. Their unwavering support, belief in my abilities, and encouragement have made my pursuit of this academic endeavor possible. To my friends, I am grateful for providing a needed balance while writing this work. Their companionship and camaraderie have been a source of strength. Lastly, but by no means least, I would like to thank Fabian Ziegler. His support, understanding, and patience have been a constant source of motivation, and I am grateful for his presence in my life.

FAQs

What is the main research topic of the document?

The document centers on using machine learning for camera-based monitoring of laser welding processes.

Who are the key academic contributors mentioned?

It acknowledges the supervisor Prof. Dr.-Ing. Michael Heizmann and the coreferent Prof. Dr. Thomas Längle, along with additional mentorship support.

Where was the doctoral research conducted?

The preface highlights collaboration during the author’s work at Trumpf Laser GmbH, in conjunction with the Karlsruhe Institute of Technology’s Institute of Industrial Information Technology.

Aug 3, 2026

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