This work was financially supported by the Ministry of Science and Higher Education, research number FN/61/AU/2024.

• Autorzy: Chmielowiec Andrzej , Żurawski Paweł , Sikorska-Czupryna Sylwia , Klich Leszek , Organiściak Patryk
• Języki publikacji: EN

Abstrakty

EN

Industrial quality control systems in mass production facilities must exhibit a very high level of defect detection efficiency. The continuous increase in quality control and process automation requirements is leading companies to increasingly experiment with artificial intelligence methods to boost efficiency. One potential application area for AI is visual inspection, which is an essential element of almost every quality control process. In this article, we propose the use of neural networks for the visual inspection of rotationally symmetric components. The presented method leverages the existence of symmetry to represent images in a polar coordinate system and to implement the learning process on data modified in this way. An undeniable advantage of the proposed algorithm is also the transition from a two- dimensional to a one-dimensional representation, which significantly reduces the demand for memory resources and the required computational power. This is particularly important in mass production processes, where the time for data analysis is relatively short. The high repeatability of images due to the mass production nature makes this model exceptionally effective, allowing not only to confirm the presence of defects but even to locate them. The obtained results are compared with the results achieved using a convolutional neural network operating on two-dimensional images.

Słowa kluczowe

EN

defect detection; quality control; neural networks; computer vision; mass production

• 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: 2024
• Tom: Vol. 18, no. 8
• Strony: 403--415
• Opis fizyczny: Bibliogr. 42 poz., fig.

Twórcy

autor

Chmielowiec Andrzej

• Faculty of Mechanics and Technology, Rzeszow University of Technology, Kwiatkowskiego Street 4, 37-450 Stalowa Wola, Poland

• https://orcid.org/0000-0001-6629-0029

autor

Żurawski Paweł

• Faculty of Mechanics and Technology, Rzeszow University of Technology, Kwiatkowskiego Street 4, 37-450 Stalowa Wola, Poland

• https://orcid.org/0000-0003-2528-3230

autor

Sikorska-Czupryna Sylwia

• Faculty of Mechanics and Technology, Rzeszow University of Technology, Kwiatkowskiego Street 4, 37-450 Stalowa Wola, Poland

• https://orcid.org/0000-0002-0063-085X

autor

Klich Leszek

• Faculty of Mechanics and Technology, Rzeszow University of Technology, Kwiatkowskiego Street 4, 37-450 Stalowa Wola, Poland

• https://orcid.org/0000-0001-6099-2417

autor

Organiściak Patryk

• Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, Wincentego Pola Street 2, 35-959 Rzeszów, Poland

• https://orcid.org/0000-0002-5277-4038

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).