Intelligent Visual Quality Control System Based on Convolutional Neural Networks for Holonic Shop Floor Control of Industry 4.0 Manufacturing Systems

• Autorzy: Oborski Przemysław , Wysocki Przemysław

Identyfikatory

DOI

10.12913/22998624/145503

• Języki publikacji: EN

Abstrakty

EN

The article presents research on industrial quality control system based on AI deep learning method. They are a part of larger project focusing on development of Holonic Shop Floor Control System for integration of machines, machine operators and manufacturing process monitoring with information flow in whole production process according to Industry 4.0 requirements. A system connecting together machine operators, machine control, process and machine monitoring with companywide IT systems is developed. It is an answer on manufacture of airplane industry requirements. The main aim of the system is full automation of information flow between a management level and manufacturing process level. Intelligent, flexible quality control system allowing for active manufacturing optimization on the base of achieved results as well as a historical data collection for further Big Data analysis is the main aim of the current research. During research number of selected AI algorithms were tested for assessing their suitability for performing tasks identified in real manufacturing environment. As a result of the conducted analyzes, Convolutional Neural Networks were selected for further study. Number of built Convolutional Neural Networks algorithms were tested using sets of data and photos from the production line. A further step of research will be focused on testing a system in real manufacturing process for able possible construct a fully functional quality control system based on the use of Convolutional Neural Networks.

Słowa kluczowe

EN

quality control; manufacturing systems; Industry 4.0; artificial intelligence; convolutional neural networks; deep learning; process monitoring; holonic shop floor control; integrated IT systems

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2022
• Tom: Vol. 16, no 2
• Strony: 89--98
• Opis fizyczny: Bibliogr. 34 poz., fig., tab.

Twórcy

autor

Oborski Przemysław

• Institute of Manufacturing Technologies, Faculty of Production Engineering, Warsaw University of Technology, Plac Politechniki 1, 00-661 Warszawa, Poland

• https://orcid.org/0000-0001-7778-4577

autor

Wysocki Przemysław

• Intel, Al. Jerozolimskie 146C, 02-305 Warszawa, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).