| 1. |
- Semeniuta, O., et al.
(författare)
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Discrete event dataflow as a formal approach to specification of industrial vision systems
- 2015
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. - 9781467381833 ; 2015-October, s. 849-854
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Konferensbidrag (refereegranskat)abstract
- The need for more flexible manufacturing systems stimulates the adoption of industrial robots in combination with intelligent computing resources and sophisticated sensing technologies. In this context, industrial vision systems play a role of inherently flexible sensing means that can be used for a variety of tasks within automated inspection, process control and robot guidance. When vision sensing is used within a large complex system, it is of particular importance to handle the complexity by introducing the appropriate formal methods. This paper overviews the challenges arising during design, implementation and application of industrial vision systems, and proposes an approach, dubbed Discrete Event Dataflow (DEDF), allowing to formally specify vision dataflow in the context of larger systems.
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| 2. |
- Semeniuta, O., et al.
(författare)
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Towards increased intelligence and automatic improvement in industrial vision systems
- 2018
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Ingår i: Procedia CIRP. - : Elsevier BV. - 2212-8271. ; 67, s. 256-261
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Konferensbidrag (refereegranskat)abstract
- Robots and in-process inspection systems equipped with machine vision solutions are used for increased flexibility and quality in automated manufacturing. Although vision systems have found wide industrial use, there are still problems regarding optimization of vision system robustness and capabilities. This paper presents a comprehensive case study of vision system functions, techniques and capabilities in an automotive 1-tiers supplier. Based on the study, the paper further describes a method for systematic improvement of industrial vision systems on a continuous basis. This is proposed to be done by establishing a data store and data analysis system, based on training machine learning models in an off-line mode using the historical data, as well as on on-line stream processing.
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| 3. |
- Semeniuta, O., et al.
(författare)
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Vision-based robotic system for picking and inspection of small automotive components
- 2016
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. - 9781509024094 ; 2016-November, s. 549-554
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Konferensbidrag (refereegranskat)abstract
- The use of vision systems for industrial robot guidance and quality control becomes much harder when the manufactured products and their components are small and possess reflective surface. To assure an effective automated visual inspection of such components, novel solutions are required, able to perform more advanced image analysis and tackle noise and uncertainty. This paper proposes a concept of multi-camera/multi-pose inspection station for star washers inspection, and presents the first results of a functional prototype implementation of it in a robotic cell. The processes of vision-guided part picking from a flexible feeder and close-range inspection in a dedicated rig are described. Solutions for the vision-based tasks of parts identification, machine learning-based classification, circular objects image analysis and star washer teeth segmentation are presented, and further directions are outlined. © 2016 IEEE.
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