FEATURES OF THE IMPLEMENTATION OF COMPUTER VISION IN THE PROBLEMS OF AUTOMATED PRODUCT QUALITY CONTROL
Nataliia Stelmakh
n.stelmakh@kpi.uaNational Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Instrument Production and Engineering (Ukraine)
https://orcid.org/0000-0003-1876-2794
Ihor Mastenko
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Instrument Production and Engineering (Ukraine)
https://orcid.org/0000-0002-2953-4589
Olga Sulima
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Mathematical Physics and Differential Equations (Ukraine)
https://orcid.org/0000-0002-5811-7717
Tetiana Rudyk
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Mathematical Physics and Differential Equations (Ukraine)
https://orcid.org/0000-0003-1121-4963
Abstract
The article analyzes the fields of application of machine vision. Special attention is focused on the application of Machine Vision in intelligent technological systems for product quality control. An important aspect is a quick and effective analysis of product quality directly at the stage of the technological process with high accuracy in determining product defects. The appropriateness and perspective of using the mathematical apparatus of artificial neural networks for the development of an intelligent technological system for monitoring the geometric state of products have been demonstrated. The purpose of this study is focused on the identification and classification of reed tuber quality parameters. For this purpose, new methods of identification and classification of quality control of various types of defects using computer vision and machine learning algorithms were proposed.
Keywords:
machine vision, intelligent technological system, quality control, neural networksReferences
Desmond K. et al.: A machine vision algorithm for quality control inspection of gears. The International Journal of Advanced Manufacturing Technology 106(1-2), 2020, 105–120.
DOI: https://doi.org/10.1007/s00170-019-04426-2
Google Scholar
Domel A. et al.: Autonomous pick and place operations in industrial production. 12th Intern. Conf. on Ubiquitous Robots and Ambient Intelligence (URAI 2015), Kintex, Goyang, Korea, 356.
DOI: https://doi.org/10.1109/URAI.2015.7358978
Google Scholar
Dyatlov E. I.: Machine vision (analytical review). Mathematical machines and systems 2, 2013, 32–40.
Google Scholar
Guo Y. et al.: Rotational projection statistics for 3D local surface description and object recognition. International journal of computer vision 105(1), 2013, 63–86.
DOI: https://doi.org/10.1007/s11263-013-0627-y
Google Scholar
Industrial Machine Vision Market by Component (Hardware (Camera, Frame Grabber, Optics, Processor), and Software (Deep Learning, and Application Specific)), Product (PC-based, and Smart Camerabased), Application, End-User. Global Forecast to 2023 [https://www.researchandmarkets.com/research/k6lrbk/global_industrial?w=5].
Google Scholar
Johnson A. E.: Spin-images: a representation for 3-D surface matching: Diss. Andrew Edie Johnson – Pittsburgh, Pennsylvania, 1997.
Google Scholar
Lisovsky A. L.: Application of neural network technologies for management development of systems. Strategic decisions and risk management 11(4), 2020, 378–389.
DOI: https://doi.org/10.17747/2618-947X-923
Google Scholar
Mastenko I. et al.: Vision yak nevidyemna chastyna intelektual’nykh tekhnolohichnykh system. Tekhnichni Nauky Ta Tekhnolohiyi 4(26), 2021, 58–65 [http://doi.org/10.25140/2411-5363-2021-4(26)-58-66].
DOI: https://doi.org/10.25140/2411-5363-2021-4(26)-58-66
Google Scholar
Mastenko І. V., Stelmakh N. V.: Generative design of a frame type construction. KPI Science News 2, 2021, 81–89.
DOI: https://doi.org/10.20535/kpisn.2021.2.236954
Google Scholar
Sahoo S. K. et al.: A Dynamic Bottle Inspection Structure. Computational Intelligence in Data Mining 711, 2019, 873–884.
DOI: https://doi.org/10.1007/978-981-10-8055-5_77
Google Scholar
Stelmakh N.: Software module for accelerated technological preparation of assembly small-scale production of devices. Visn. NTUU "KPI". Eng. 54, 2009, 12–17.
Google Scholar
Tymchyshyn R. M. et al.: Modern approaches to solving computer vision problems. Control systems and machines 6, 2018, 46–73.
Google Scholar
Zhong Y., Fengyu X., Yue W.: Analysis and experiment of workpiece quality detection based on industrial robot. 23rd Intern. Conf. on Mechatronics and Machine Vision in Practice (M2VIP), 2016, 1–6.
DOI: https://doi.org/10.1109/M2VIP.2016.7827291
Google Scholar
Zuxiang W., Lei Z., Junpeng F.: Design of safety capacitors quality inspection robot based on machine vision. 1st Intern. Conf. on Electronics Instrumentation Information Systems (EIIS), 2017, 1–4.
DOI: https://doi.org/10.1109/EIIS.2017.8298545
Google Scholar
Authors
Nataliia Stelmakhn.stelmakh@kpi.ua
National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Instrument Production and Engineering Ukraine
https://orcid.org/0000-0003-1876-2794
Authors
Ihor MastenkoNational Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Instrument Production and Engineering Ukraine
https://orcid.org/0000-0002-2953-4589
Authors
Olga SulimaNational Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Mathematical Physics and Differential Equations Ukraine
https://orcid.org/0000-0002-5811-7717
Authors
Tetiana RudykNational Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Department of Mathematical Physics and Differential Equations Ukraine
https://orcid.org/0000-0003-1121-4963
Statistics
Abstract views: 272PDF downloads: 216
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Most read articles by the same author(s)
- Nataliia Stelmakh, Oleg Belman, DEVELOPMENT OF AUTOMATION OF WASTE SORTING AS AN INTEGRAL PART OF ENVIRONMENTAL PROTECTION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 12 No. 2 (2022)
- Nataliia Stelmakh, Svitlana Mandrovska, Roman Galagan, APPLICATION OF RESNET-152 NEURAL NETWORKS TO ANALYZE IMAGES FROM UAV FOR FIRE DETECTION , Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska: Vol. 14 No. 2 (2024)
