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This article discusses a predictive quality management system that aims to eliminate repair technologies by exploiting the cognitive capability of manufacturing facilities in the manufacturing process. During production, deviations from the required quality parameters such as strip flatness, strip profile, and non-achievement of the required mechanical properties of dimensional variations occur, and their correction or correction requires repair technologies beyond the standard processes. The metallurgical process itself is energy and financially demanding. Repairing technologies represent added production costs and environmental burdens not only in the form of high-energy consumption but also in the production of harmful substances that have a negative impact on the environment. The production of solid dust impurities, the production of gaseous exhalations, high water consumption, environmental warming and water pollution, and the formation of slag ash are just a few negative aspects of metallurgical production. Steel producers make great efforts to achieve the required quality parameters and reduce the cost of repair technologies.
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Rocznik
Tom
Strony
271--276
Opis fizyczny
Bibliogr. 14 poz., fig.
Twórcy
autor
- Faculty of Mechanical Engineering, Technical University of Kosice, Slovak Republic
autor
- Faculty of Mechanical Engineering, Technical University of Kosice, Slovak Republic
autor
- Faculty of Commerce, University of Economics in Bratislava, Slovak Republic
autor
- Faculty of Mechanical Engineering, Technical University of Kosice, Slovak Republic
Bibliografia
- 1. Gilchrist, A. Introducing Industry 4.0. In: Industry 4.0. Apress, Berkeley, CA, 2016: 195-215.
- 2. Gilchrist, A. The technical and business innovators of the industrial internet. In: Industry 4.0. Apress, Berkeley, CA, 2016: 33-64.
- 3. Fletcher, R. Practical methods of optimization. John Wiley & Sons, 2013.
- 4. Takala, J., Malindžák, D., Straka, M. Manufacturing Strategy. Applying the Logistics Models. Vaasan yliopiston julkaisuja. Selvityksiä ja Raportteja, 2017, 138.
- 5. Tomek, G., Vavrova, V. Production management, 1999.
- 6. Rastogi, M. K. Production and operation management. Laxmi Publications Ltd., 2010.
- 7. Daneshjo, N., Rudy, V., Repková, K., Mareš, A., Kováč, J., Jahnátek, J. Rusnák, J. Intelligent industrial engineering – Innovation potential. FedEx Print & Ship Center, USA, 2018.
- 8. Svetlík, J., Malega, P., Rudy, V., Rusnák, J., Kováč, J. Application of innovative methods of predictive control in projects involving intelligent steel processing production systems. Materials, 2021, 14(7): 1641.
- 9. Miśkiewicz, R., Wolniak, R. Practical application of the Industry 4.0 concept in a steel company. Sustainability, 2020, 12(14): 5776.
- 10. Dobrzański, L.A. Role of materials design in maintenance engineering in the context of industry 4.0 idea. Journal of Achievements in Materials and Manufacturing Engineering, 2019, 96(1).
- 11. Horst, D.J., Duvoisin, C.A., de Almeida Vieira, R. Additive manufacturing at Industry 4.0: a review. International Journal of Engineering and Technical Research, 2018, 8(8).
- 12. Kavan, M. Production and operation management. Praha: Grada Publishing, 2002
- 13. Roberts, W.: Cold rolling of steel. Marcel Dekker New York Inc., 1978.
- 14. Rudy, V., Rusnák, J., Mares, A.: Application of Industry 4.0 tools in metallurgy processes. Transfer Innovation. Kosice TU, 2018, 38: 57-59.
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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9a4b1a73-4350-436f-8a70-b0de145d7b30