Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This paper focuses on the optimization of production processes in a tool-room with a specific emphasis on grinding operations. The main objective is to identify and implement approaches that enhance production efficiency, minimize downtime, and reduce manufacturing costs. Through an analysis of the current state of production processes, key factors negatively affecting productivity were identified, including underutilization of tools and inefficient planning. The paper presents a methodology for evaluating the performance of production systems, which includes the use of the OEE (Overall Equipment Effectiveness) tool and workflow analysis. Based on the collected data, specific improvements were proposed, such as the introduction of modern tools, optimization of workflows, and the implementation of sensor-based predictive maintenance. These measures resulted in a significant reduction in production downtime and an increase in workplace productivity. The results demonstrate that the proper combination of technical innovations, effective planning, and monitoring of production processes can lead to significant improvements in the performance of production systems. The contribution of this study lies in providing practical solutions that can be applied across various industrial sectors while highlighting the importance of integrating modern technological tools into production processes. This article is valuable for professionals in industrial production, quality management, and operations engineering who seek effective strategies for optimizing production operations and increasing the competitive advantage of their enterprises.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
17--31
Opis fizyczny
Bibliogr. 15 poz., fig., tab.
Twórcy
autor
- Faculty of Mechanical Engineering, Technical University of Kosice, Slovak Republic
autor
- Faculty of Commerce, University of Economics in Bratislava, Dolnozemska cesta 1, 852 35 Bratislava 5, Slovak Republic
Bibliografia
- 1. Sabadka, D., Molnar, V., Fedorko, G. The use of lean manufacturing techniques–SMED analysis to optimization of the production process. Advances in Science and Technology. Research Journal. 2017; 11(3): 187–195. https://doi.org/10.12913/22998624/76067.
- 2. Weichert, D., Link, P., Stoll, A., Rüping, S., Ihlenfeldt, S., & Wrobel, S. A review of machine learning for the optimization of production processes. The International Journal of Advanced Manufacturing Technology. 2019; 104(5): 1889–1902. https://doi.org/10.1007/s00170-019-03988-5.
- 3. Nektegyaev, G. G., Borisov, A. I. Ways of automation and optimization of measuring instruments. In IOP Conference Series. Materials Science and Engineering. 2020; 862(4): 042052. https://doi.org/10.1088/1757-899X/862/4/042052.
- 4. Song, Y. Design and optimization of thermal instruments: Comparing traditional and modern technologies. Journal of Electrotechnology. Electrical Engineering and Management. 2023; 6(5): 45–50. https://doi.org/10.23977/jeeem.2023.060506.
- 5. Yasir, A. S. H. M., Mohamed, N. M. Z. N. Assembly line efficiency improvement by using WITNESS simulation software. In IOP Conference Series: Materials Science and Engineering. 2018; 319(1): 012004. https://doi.org/10.1088/1757-899X/319/1/012004.
- 6. Vysocký, T., Marcinčinová, NĽ., Marcinčinová, N.E. Simulation as a designed tool for material flow analysis by means of Witness Horizon. In MATEC - EDP Sciences - Web of Conferences. 2023; 137: 01020. https://doi.org/10.1051/matecconf/201713701020.
- 7. Wells, S., Tamir, O., Gray, J., Naidoo, D., Bekhit, M., Goldmann, D. Are quality improvement collaboratives effective? A systematic review. BMJ quality and safety. 2018; 27(3): 226–240. https://doi.org/10.1136/bmjqs-2017-006926.
- 8. Dohale, V. D., Akarte, M. M., Verma, P. Determining the process choice criteria for selecting a production system in a manufacturing firm using a Delphi technique. In 2019 IEEE international conference on industrial engineering and engineering management. 2019; 1265–1269. https://doi.org/10.1109/IEEM44572.2019.8978820.
- 9. Röder, A., Tibken, B. A Methodology for modeling inter-company supply chains and for evaluating a method of integrated product and process documentation. European Journal of Operational Research. 2006; 169(3): 1010–1029. https://doi.org/10.1016/j.ejor.2005.02.006.
- 10. Drouillet, C., Karandikar, J., Nath, C., Journeaux, A. C., El Mansori, M., Kurfess, T. Tool life predictions in milling using spindle power with the neural network technique. Journal of Manufacturing Processes. 2016; 22: 161–168. https://doi.org/10.1016/j.jmapro.2016.06.003.
- 11. Maleki, M. R., Amiri, A. Simultaneous monitoring of multivariate-attribute process mean and variability using artificial neural networks. Journal of Quality Engineering and Production Optimization. 2015; 1(1): 43–54. https://doi.org/10.1080/25728406.2015.1112673.
- 12. Borisut, P., Nuchitprasittichai, A. Optimization of methanol production via CO2 hydrogenation: comparison of sampling techniques for process modeling. In IOP Conference Series: Materials Science and Engineering. 2020; 778(1): 012088. IOP Publishing. https://doi.org/10.1088/1757-899X/778/1/012088.
- 13. Okorocha, I. T., Chinwuko, C. E., Mgbemena, C. E., Mgbemena, C. O. Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies. International Journal of Industrial Optimization. 2020; 1(2): 61. https://doi.org/10.1007/s11595-020-0237-x.
- 14. Chabbi, A., Yallese, M. A., Nouioua, M., Meddour, I., Mabrouki, T., Girardin, F. Modeling and optimization of turning process parameters during the cutting of polymer (POM C) based on RSM, ANN, and DF methods. The International Journal of Advanced Manufacturing Technology. 2017; 91: 2267–2290. https://doi.org/10.1007/s00170-016-9316-7.
- 15. Ferro, R., Cordeiro, G. A., Ordóñez, R. E., Beydoun, G., Shukla, N. An optimization tool for production planning: A case study in a textile industry. Applied Sciences. 2021; 11(18): 8312. https://doi.org/10.3390/app11188312.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c8ecdcfc-0f97-4675-8c8f-ca99ad31d3bf
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