Abstract
Despite advancements in metallography automation, sample preparation remains largely semi-automated with isolated subprocesses like sectioning, grinding, and polishing. Leveraging modern technologies such as collaborative robotics, AI-driven computer vision, and advanced sensors could enable fully integrated automation. However, the diversity of processes requires skilled human oversight. Integrating user-friendly cobot interfaces may promote a synergistic workspace that enhances safety, reduces monotony, and supports complex studies and documentation aligned with open science principles. Our study explores cost-effective mini robots in critical preparation stages, highlighting steps toward complex automation in metallography.
Kurzfassung
In der Metallographie werden Proben trotz Fortschritten bei der Automatisierung nach wie vor weitgehend halbautomatisch über isolierte Teilprozesse wie Schneiden, Schleifen und Polieren präpariert. Mit modernen Technologien wie kollaborativer Robotik, KI-gesteuerter Computer Vision und hochentwickelten Sensoren wäre eine vollintegrierte Automatisierung möglich. Allerdings müssen die verschiedenartigen Prozesse von einer qualifizierten Fachkraft überwacht werden. Mit der Einbindung benutzerfreundlicher Cobot-Schnittstellen (Cobot = kollaborativer Roboter) kann eine synergetische Arbeitsumgebung geschaffen werden, die mehr Sicherheit gewährleistet, die Monotonie verringert und den Weg frei macht für komplexe Untersuchungen und Dokumentationen, die den Grundsätzen der offenen Wissenschaft gerecht werden. Unsere Untersuchung beschäftigt sich mit der Nutzung von kostengünstigen Minirobotern für kritische Phasen der Präparation und präsentiert Schritte hin zu einer komplexen Automatisierung in der Metallographie.
About the authors
Jan Čermák is a PhD student at BUT and member of the Microscopy for Materials Science group at ISI. As a graduate in robotics, he integrates process automation in the metallography laboratory and in current research he deals with the application of deep learning methods for AHSS microstructure characterization.
Ondřej Ambrož is a PhD candidate at BUT. After completing his master’s degree in foundry technology, he was employed at the ŽĎAS steelworks as a melter-operator-metallurgist. Since 2018 he has been working at the ISI of the CAS where he is responsible for all metallographic operations and development of new methods.
6 Acknowledgment
The authors acknowledge the funding received from the Lumina Quaeruntur fellowship established by the Czech Academy of Sciences (LQ100652201).
6 Danksagung
Die Autoren bedanken sich für die finanzielle Unterstützung durch das Lumina Quaeruntur- Stipendium der tschechischen Akademie der Wissenschaften (LQ100652201).
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