Yevhen Bondarenko

Researches

Publications

Journal / Periodical: AIP Conference Proceedings
Authors: Terkaj, Walter; Pessot, Elena; Kuts, Vladimir; Bondarenko, Yevhen; Pizzagalli, Simone Luca; Kleine, Kari
Year: 2024
Journal / Periodical: XR and Metaverse: Proceedings of the 8th International XR-Metaverse Conference 2023, Las Vegas, USA
Authors: Bondarenko, Yevhen; Kuts, Vladimir; Pizzagalli, Simone; Nutonen, Karle; Murray, Niall; O’Connell, Eoin
Year: 2024
Journal / Periodical: Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition
Authors: Pizzagalli, S. L.; Bondarenko, Y.; Baykara, B. C.; Niidas, A.; Kuts, V.; Kerm, M.; Otto, T.
Year: 2023

Projects

Year: 2023 - 2028
For the development of the field of human-robot cooperation, a development and test laboratory for collaborative robotics and process-adaptive devices will be created based on the Virumaa Innovation Centre of Digitalisation and Green Technologies, Virumaa College, and Taltech's headquarters. In the created laboratory, it will be possible to study the psychological aspects of human-machine co-creation, workplace design, etc. In addition, adaptability of equipment/physical systems to production processes. All this in both real and digital (augmented reality) environments, based on the Industry X.0 concept.
Year: 2023 - 2026
This project focuses on the development and integration of Extended Reality and existing digital tools to support advanced engineering education in manufacturing. The purpose of the XREN is to bring back the results of research activities in the field of digital manufacturing to the engineering students. Since, process modelling, analysis, virtual and augmented reality, as well as the role of the human workers in the factories have been among the main research topics in manufacturing. Therefore, the project aims to experiment, test and validate learning approaches based on XR technologies, focusing the attention on: • Development of a VR environment to support learning activities in the area of manufacturing, namely in the design and analysis of manufacturing systems. • Development of an AR approach to support learning activities in the area of mechanical design and maintenance process. • Develop approaches and methodologies to analyze and evaluate the learning mechanisms based on these technologies.