Background and activities
Oleksandr Semeniuta is an Associate Professor in Cyber-Physical Systems at the Department of Manufacturing and Civil Engineering (IVB).
Oleksandr received Ph.D. degree in Automation from Chalmers University of Technology, Sweden, M.Sc. degree in Sustainable Manufacturing from Gjøvik Univesity College, Norway, and B.Sc. degree in Automation and Computer-Integrated Technologies from Igor Sikorsky Kyiv Polytechnic Institute, Ukraine. In 2013-2014 he was with SINTEF Raufoss Manufacturing AS, conducting research within calibration of robotic vision systems and holonic manufacturing control.
His current research interests include machine vision, machine learning, distributed control systems, and event-driven architectures for robotics and automation. A central theme of his research is smooth transition from ad-hoc prototyping to building well-structured computing and control systems.
Oleksandr is the author of several open source projects available at https://github.com/semeniuta.
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2021) Investigating the Dimensional and Geometric Accuracy of Laser-Based Powder Bed Fusion of PA2200 (PA12): Experiment Design and Execution. Applied Sciences. vol. 11 (5).
- (2021) Subset-based stereo calibration method optimizing triangulation accuracy. PeerJ Computer Science.
- (2020) Extracting shape features from a surface mesh using geometric reasoning. Procedia CIRP. vol. 93.
- (2020) Tolerancing from STL data: A Legacy Challenge. Procedia CIRP. vol. 92.
- (2019) EPypes: a framework for building event-driven data processing pipelines. PeerJ Computer Science. vol. 2019 (2).
- (2019) Event-driven industrial robot control architecture for the Adept V+ platform. PeerJ Computer Science. vol. 2019 (7).
- (2018) Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework. Procedia CIRP. vol. 67.
- (2018) Towards increased intelligence and automatic improvement in industrial vision systems. Procedia CIRP. vol. 67.
- (2016) Analysis of Camera Calibration with Respect to Measurement Accuracy. Procedia CIRP. vol. 41.
- (2016) Vision-based robotic system for picking and inspection of small automotive components. IEEE International Conference on Automation Science and Engineering. vol. 2016-November.
Part of book/report
- (2020) Deburring Using Robot Manipulators: A Review. Proceeding of 3rd International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS2020).
- (2019) Application of Machine Learning Methods to Improve Dimensional Accuracy in Additive Manufacturing. Advanced Manufacturing and Automation VIII Proceedings IWAMA 2018.
- (2019) MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices. Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion.
- (2018) Flexible Image Acquisition Service for Distributed Robotic Systems. 2018 Second IEEE International Conference on Robotic Computing (IRC).
- (2015) Discrete event dataflow as a formal approach to specification of industrial vision systems. Automation Science and Engineering (CASE), 2015 IEEE International Conference on.
- (2018) Flexible Composition of Robot Logic with Computer Vision Services. 2018. ISBN 978-91-7597-777-5.
- (2017) Control of visually guided event-based behaviors in industrial robotic systems. 2017. Licentiatavhandlingar vid Chalmers tekniska högskola (R001/2017).