Identyfikatory
DOI
Warianty tytułu
Języki publikacji
Abstrakty
The state of the technical infrastructure determines the degree to which the quality requirements of products are met and has a significant impact on occupational safety. The purpose of this study is to build a universal model for supporting quality management, which allows the effective implementation of a wide-ranging research path supporting the evaluation of the relationship between the degree of modernity of product processing technology and the quality of the final product and the level of occupational safety. The developed model is verified by its implementation in one of the turning stations. A practical test of the quality management support model confirms that the practice of conducting analyzes of the level of modernity of infrastructure with its application contributed to identifying critical machine components, examining factors affecting the quality of technological operations, reducing uncertainty and the risk of risky events, and conducting activities in line with the concept of continuous improvement. The course of action detailed in the model makes it possible to determine the relationships that exist between key categories of factors and critical product defects, and accidents and near-misses. This allows for the proposal of adequate improvement measures. Further studies concern the implementation of the model at other workstations in the foundry company.
Rocznik
Tom
Strony
5--13
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
autor
- Rzeszow University of Technology 12 Powstańców Warszawy Ave., 35-959 Rzeszów, Poland
autor
- Rzeszow University of Technology 12 Powstańców Warszawy Ave., 35-959 Rzeszów, Poland
Bibliografia
- 1. Agergaard, J.K., Sigsgaard, K.V., Mortensen, N.H., Ge, J. & Hansen, K.B. (2022) Quantifying the impact of early-stage maintenance clustering. Journal of Quality in Maintenance Engineering 29(5), pp. 1–15, doi: 10.1108/ JQME-07-2021-0056.
- 2. Bumba, A., Gomes, M., Jesus, C. & Lima, R. (2023) KPI tree – a hierarchical relationship structure of key performance indicators for value streams. Production Engineering Archives 29(2), pp. 175–185, doi: 10.30657/pea.2023.29.21
- 3. Daryani, S.M., Khodaverdi, Y., Rasouli, E. & Ehareghi, B. (2012) The importance of knowledge management technologies in performance improvement of organizations. Life Science Journal – Acta Zhengzhou University Overseas Edition 9(4), pp. 4695–4699.
- 4. Gawlik, R. (2016) Methodological aspects of qualitative-quantitative analysis of decision-making processes. Management and Production Engineering Review 7(2), pp. 3–11, doi: 10.1515/mper-2016-0011.
- 5. Gries, T. & Naudé, W. (2010) Entrepreneurship and structural economic transformation. Small Business Economics 34, pp. 13–29, doi: 10.1007/s11187-009-9192-8.
- 6. Holtzer, M., Dańko, R. & Żymankowska-Kumon, S. (2012) Foundry industry – current state and future development. Metalurgija 51(3), pp. 337–340.
- 7. Hys, K. (2015) Zjawisko koncentracji i delokalizacji w branży motoryzacyjnej. Handel Wewnętrzny 5(358), pp. 163–175.
- 8. Hys, K. & Hawrysz, L. (2012) Corporate social responsibility reporting. China-USA Business Review 11(11), pp. 1515–1524.
- 9. Ismayilov, V.I., Almasov, N.N., Musayev, N.S. & Samedova, A.Q. (2021) Influence of internal production conditions on the efficiency and competitiveness of enterprises. Future Business Journal 7, 40, doi: 10.1186/s43093-021-00086-5.
- 10. Kim, D., Lee, S. & Kim, D. (2021) An Applicable Predictive Maintenance Framework for the Absence of Run-toFailure Data. Applied Sciences 11(11), 5180, doi: 10.3390/ app11115180
- 11. Klimecka-Tatar, D. & Ingaldi, M. (2020) Assessment of the Technological Position of a Selected Enterprise in the Metallurgical Industry. Materials Research Proceedings 17, pp 72–78, doi: 10.21741/9781644901038-11.
- 12. Kozłowski, J., Sika, R., Górski, F. & Ciszak, O. (2019) Modeling of Foundry Processes in the Era of Industry 4.0. In: Advances in Design, Simulation and Manufacturing DSMIE 2018. Lecture Notes in Mechanical Engineering. Springer, Cham, pp. 62–71, doi: 10.1007/978-3-319-93587- 4_7.
- 13. Ligarski, M.J., Rozalowski, B. & Kalinowski, K. (2021) A study of the human factor in industry 4.0 based on the automotive industry. Energies 14(20), 6833, doi: 10.3390/ en14206833.
- 14. Mahapatra, M.S. & Shenoy, D. (2022) Lean maintenance index: a measure of leanness in maintenance organizations. Journal of Quality in Maintenance Engineering 28(4), pp. 791–809, doi: 10.1108/JQME-08-2020-0083.
- 15. Malinowski, P. (2021) Casting production management system. Metalurgija 60(3–4), pp. 451–453.
- 16. Miskinis, G.V. (2021) Transformation of the modern foundry. International Journal of Metalcasting 15, pp. 1118–1128, doi: 10.1007/s40962-021-00645-0.
- 17. Pacana, A. & Czerwińska, K. (2019a) Analysis of the causes of control panel inconsistencies in the gravitational casting process by means of quality management instruments. Production Engineering Archives 25(25), pp. 12–16, doi: 10.30657/pea.2019.25.03.
- 18. Pacana, A. & Czerwińska, K. (2019b) Comprehensive improvement of the surface quality of the diesel engine piston. Metalurgija 58(3–4), pp. 329–332.
- 19. Pacana, A. & Czerwińska, K. (2020) Comparative tests of the quality of the piston combustion chamber for a diesel engine. Tehnički Vjesnik – Technical Gazette 27(3), pp. 1021– 1024, doi: 10.17559/TV-20190112193319.
- 20. Pacana, A. & Czerwińska, K. (2023) Indicator analysis of the technological position of a manufacturing company. Production Engineering Archives 29(2), pp. 162–167, doi: 10.30657/pea.2023.29.19.
- 21. Pietraszek, J., Radek, N. & Goroshko, A.V. (2020) Challenges for the DOE methodology related to the introduction of Industry 4.0. Production Engineering Archives 26(4), pp. 190–194, doi: 10.30657/pea.2020.26.33.
- 22. Silvestri, L., Forcina, A., Introna, V., Santolamazza, A. & Cesarotti, V. (2020) Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Computers in Industry 123, 10335, doi: 10.1016/j. compind.2020.103335.
- 23. Singh, R.K. & Gurtu, A. (2022) Prioritizing success factors for implementing total productive maintenance (TPM). Journal of Quality in Maintenance Engineering 28(4), pp. 810–830, doi: 10.1108/JQME-09-2020-0098.
- 24. Siwiec, D. & Pacana, A. (2021) Method of improve the level of product quality. Production Engineering Archives 27(1), pp. 1–7, doi: 10.30657/pea.2021.27.1.
- 25. Skotnicka-Zasadzień, B. (2010) The use of tools improving quality in the production process. Scientific Journals Maritime University of Szczecin, Zeszyty Naukowe Akademia Morska w Szczecinie 24(96), pp. 105–111.
- 26. Tran Anh, D., Dąbrowski, K. & Skrzypek, K. (2018) The predictive maintenance concept in the maintenance department of the “industry 4.0” production enterprise. Foundations of Management 10(1), pp. 283–292, doi: 10.2478/ fman-2018-0022.
- 27. Ulewicz, R., Czerwińska, K. & Pacana, A. (2023) A rank model of casting non-conformity detection methods in the context of industry 4.0. Materials 16, 723, doi: 10.3390/ ma16020723.
- 28. Ulewicz, R., Mazur, M. & Novy, F. (2019) The impact of lean tools on the level of occupational safety in metals foundries. Proceedings 28th International Conference on Metallurgy and Materials (METAL 2019), pp. 2013–2019, doi: 10.37904/metal.2019.992.
- 29. Wolniak, R. (2019) Downtime in the Automotive Industry Production Process – Cause Analysis. Quality Innovation Prosperity – Kvalita Inovacia Prosperita 23(2), pp. 101– 118, doi: 10.12776/QIP.V23I2.1259.
- 30. Wolniak R., Szeptuch, A. & Ziecina, G. (2017) Analysis of behavior of management in an international metallurgical company, using Cameron and Quinn typology. E-mentor 2 (69), pp. 60–69, doi: 10.15219/em69.1297.
- 31. Wu, S.-H., Lin, F.-J. & Perng, C. (2022) The affecting factors of small and medium enterprise performance. Journal of Business Research 143, pp. 94–104, doi: 10.1016/j. jbusres.2022.01.025.
- 32. Zhang, C., Zhang, Y.-D., Dui, H.-Y., Wang, S.-P. & Tomovic, M.M. (2021) Importance measure-based maintenance strategy considering maintenance costs. Maintenance and Reliability 24(1), pp. 15–24, doi: 10.17531/ein.2022.1.3.
- 33. Zhang, X.Q., Jiang, H., Zheng, B., Li. Z.S. & Gao, H.Y. (2022) Optimal maintenance period and maintenance sequence planning under imperfect maintenance. Quality and Reliability Engineering International, doi: 10.1002/ qre.3192.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-62b08047-8090-4e53-bfe9-4cff16f3c8fb