DOI: https://doi.org/10.32515/2664-262X.2025.11(42).135-142
Modernisation of Foundry Production with Industrial Robots and Manipulators
About the Authors
Bohdan Tsymbal, Associate Professor, Doctor of Science in Public Administration, Professor of the Department of Occupational Safety and Environmental Safety of the Educational and Scientific Institute of Management and Population Safety, National University of Civil Protection of Ukraine, Cherkasy, Ukraine, Associate Professor of the Department of Automation, Electrical and Robotic Systems of the Faculty of Production Automation and Digital Technologies, LTD "Technical University "Metinvest Polytechnic", Zaporizhzhia, Ukraine, ORCID: 0000-0002-2317-3428, e-mail: tsembalbogdan@ukr.net
Nataliya Karyavkina, Student, LLC "Technical University "Metinvest Polytechnic", Zaporizhia, Ukraine, ORCID: 0009-0000-2457-6280, e-mail: karavkinanatalia@gmail.com
Abstract
The modernization of foundry production through industrial robots and manipulators represents a significant advancement in automation, efficiency, and occupational safety. The foundry industry plays a crucial role in the manufacturing sector, providing high-quality metal products for various applications. However, traditional foundry processes are often labor-intensive, hazardous, and prone to inefficiencies. In recent years, the integration of robotics and automated technologies has become an essential aspect of improving productivity, ensuring precision, and enhancing workplace safety. This study focuses on the application of industrial drones in foundry production, particularly for material transportation, coating application, and quality control. The research explores the feasibility of integrating the DJI Matrice 300 RTK drone into the casting process, analyzing its potential benefits in optimizing logistics, minimizing defects, and improving overall process efficiency. The use of drones in foundry operations offers several advantages, including the ability to operate in high-temperature environments, automate routine tasks, and reduce human exposure to hazardous conditions. To achieve these objectives, the study employs a combination of theoretical analysis, mathematical modeling, and experimental testing. A mathematical model of drone movement within the foundry environment is developed to optimize flight paths, minimize energy consumption, and ensure precise navigation. Additionally, simulations are conducted to assess the impact of high temperatures and airborne particles on drone performance. Experimental trials are carried out in real production conditions to validate the proposed approach and evaluate the efficiency of drone-assisted operations. The results of the study indicate that industrial drones can significantly improve foundry logistics by reducing material transportation time and ensuring the precise application of protective coatings. The implementation of automated aerial systems enhances quality control by enabling real-time monitoring of casting processes and early defect detection. Moreover, the reduction of human involvement in hazardous tasks contributes to workplace safety and minimizes occupational risks. Based on the findings, recommendations are proposed for optimizing the integration of drones into foundry production. These include refining navigation algorithms, enhancing thermal protection measures, and developing advanced sensor technologies for improved defect detection. The implementation of these recommendations can lead to higher efficiency, reduced waste, and increased competitiveness in the foundry industry. The study's outcomes have been applied in practical settings, including their adoption at PJSC "Kamet Stal" and incorporation into educational programs to enhance training in industrial automation. The research highlights the transformative potential of drone technology in modernizing foundry production and underscores the importance of continued innovation in automation and robotics. Future research directions include exploring alternative energy sources for drones, improving their adaptability to extreme environmental conditions, and expanding their functionality for broader industrial applications.
Keywords
automation, foundry production, industrial drones, modernization, robotics, Industry 4.0, occupational safety
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References
1. Doroshenko, V., & Yanchenko, O. (2020). Improving the resource efficiency of foundry due to the assembly of conveyor and rotor-conveyor lines by robots. Modern Technology, Materials and Design in Construction, 27(2), 179–186. https://doi.org/10.31649/2311-1429-2019-2-179-186
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4. Doroshenko, V.S. (2016). Lyttia po hazifikovanykh modeliakh z krystalizatsiieiu metalu pid tyskom. Lyvarne vyrobnytstvo, (1), 25–28 [in Ukrainian].
5. Kaliuzhnyi, P.B., Brodovyi, O.V., Doroshenko, V.S., & Neima, O.V. (2024). 3D-heneratsiia porystykh struktur dlia drukuvannia lyvarnykh modeli, shcho hazifikuiutsia v lyvarnii formi. Novi materialy i tekhnolohii v mashynobuduvanni, (5). https://doi.org/10.20535/2519-450x.5.2024.319032 [in Ukrainian].
6. Doroshenko, V.S. (2019). Kontseptsiia rotorno-konveiiernoho kompleksu dlia lytia po hazifikovanym modeliakh i termoobrabotky viddlivok. Metall i lytie Ukrainy, (1–2), 31–40. http://jnas.nbuv.gov.ua/article/UJRN-0001001769 [in Ukrainian].
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12. Lynch, K.M., & Park, F.C. (2017). Modern robotics: Mechanics, planning and control. Cambridge: Cambridge University Press. http://modernrobotics.org
13. Goldstein, H., Poole, C., & Safko, J. (2002). Classical mechanics. Addison-Wesley. https://surl.li/mrdcyr
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15. Batchelor, G.K. (2000). An introduction to fluid dynamics. Cambridge University Press. https://doi.org/10.1017/CBO978051180095
16. Heaton, I., Goodfellow, J., Ian, Y., Bengio, Y., & Courville, A. (2017). Deep learning. Genetic Programming and Evolvable Machines, 19(1–2), 305–307. https://doi.org/10.1007/s10710-017-9314-z
Citations
1. Doroshenko V., Yanchenko O. Improving the resource efficiency of foundry due to the assembly of conveyor and rotor-conveyor lines by robots. Modern Technology, Materials and Design in Construction. 2020. Vol. 27. № 2. P. 179–186. DOI: 10.31649/2311-1429-2019-2-179-186
2. Дорошенко В. С. Метод нейтралізації газів, що виділяються з ливарної форми при литві за газифікованими моделями. Ливарне виробництво. 2021. № 9. С. 8-14.
3. Дорошенко В.С. Комплектація роботами конвеєрних та роторно-конвеєрних ліній ливарного виробництва. Лиття України. 2019. № 6. С. 21-23.
4. Дорошенко, В.С. Лиття по газифікованих моделям з кристалізацією металу під тиском. Ливарне виробництво. 2016. № 1. С. 25-28.
5. Калюжний П. Б., Бродовий О. В., Дорошенко В. C., Нейма О. В. 3d-генерація пористих структур для друку ливарних моделей, що газифікуються в ливарній формі. Нові матеріали і технології в машинобудуванні (Праці Міжнародної науково-технічної конференції). 2024. № 5. DOI: 10.20535/2519-450x.5.2024.319032
6. Дорошенко В. С. Концепція роторно-конвейєрного комплексу для літа по газофікованим моделям і термообробки відливок. Металл и литье Украины. 2019. № 1-2. С. 31-40. URL: http://jnas.nbuv.gov.ua/article/UJRN-0001001769
7. Song K.-T., Ou S.-Q., Yang C.-A., Sun Y.-X., Kang L.-R., Wang Z.-Y., Wang Y.-S., Lu P.-C., Ko C.-L., Chen Y. H.. Scheduling and control of a wafer transfer robot for foundry equipment innovation competition. IFAC-PapersOnLine. 2019. Vol. 52. № 15. P. 627–632. DOI: 10.1016/j.ifacol.2019.11.654
8. Калюжний П.Б., Шинський O.Й., Дорошенко В.С. Удосконалення ступінчастої ливникової системи для лиття за моделями, що газифікуються. Литво. Металургія. 2024 : Матеріали XX Ювіл. Міжнар. науково-практ. конф., м. Харків, 30 трав. 2024 р. Харків. 2024. С. 123–126. URL: https://repository.kpi.kharkov.ua/handle/KhPI-Press/79750
9. Kaliuzhnyi P., Doroshenko V., Neima O. Casting of combined polymer patterns that are gasified. Casting processes. 2023. Vol. 152. № 2. P. 49–55. DOI: 10.15407/plit2023.02.049
10. ABB Robotics magazine. International customer magazine from ABB Robotics. 2015. № 2. Publisher: ABB Robotics AB. URL: https://surl.li/wvzrjm
11. Yang, Y., Zheng, Y., Xu, Y., Yang, M., Boyang, B., & Liu, F. Robotic applications of foundry industry in china. У Proceedings of the 72nd world foundry congress. 21-25th May 2016. Nagoya. URL: https://www.senkyo.co.jp/shishido/wfc2016/pdf/O-153.pdf
12. Lynch K. M., Park F. C. Modern robotics: mechanics, planning and control. Cambridge University Press, 2017. 544 p. URL: http://modernrobotics.org
13. Goldstein H., Poole C., Safko J., Classical Mechanics, Addison-Wesley, 2002. URL: https://surl.li/mrdcyr
14. Bertsekas D., Dynamic Programming and Optimal Control, Athena Scientific, 2012. URL: https://surl.li/srkwtv
15. Batchelor G. K., An Introduction to Fluid Dynamics, Cambridge University Press. 2000. DOI: 10.1017/CBO978051180095
16. Heaton I., Goodfellow J. Ian, Bengio Y., Courville A.: Deep learning. Genetic programming and evolvable machines. 2017. Vol. 19, № 1-2. P. 305–307. URL: DOI: 10.1007/s10710-017-9314-z
Copyright (c) 2025Bohdan Tsymbal, Nataliya Karyavkina
Modernisation of Foundry Production with Industrial Robots and Manipulators
About the Authors
Bohdan Tsymbal, Associate Professor, Doctor of Science in Public Administration, Professor of the Department of Occupational Safety and Environmental Safety of the Educational and Scientific Institute of Management and Population Safety, National University of Civil Protection of Ukraine, Cherkasy, Ukraine, Associate Professor of the Department of Automation, Electrical and Robotic Systems of the Faculty of Production Automation and Digital Technologies, LTD "Technical University "Metinvest Polytechnic", Zaporizhzhia, Ukraine, ORCID: 0000-0002-2317-3428, e-mail: tsembalbogdan@ukr.net
Nataliya Karyavkina, Student, LLC "Technical University "Metinvest Polytechnic", Zaporizhia, Ukraine, ORCID: 0009-0000-2457-6280, e-mail: karavkinanatalia@gmail.com
Abstract
Keywords
Full Text:
PDFReferences
1. Doroshenko, V., & Yanchenko, O. (2020). Improving the resource efficiency of foundry due to the assembly of conveyor and rotor-conveyor lines by robots. Modern Technology, Materials and Design in Construction, 27(2), 179–186. https://doi.org/10.31649/2311-1429-2019-2-179-186
2. Doroshenko, V.S. (2021). Metod neitralizatsii haziv, shcho vydiliaiutsia z lyvarnoi formy pry lytvi za hazifikovanymy modeliami. Lyvarne vyrobnytstvo, (9), 8–14 [in Ukrainian].
3. Doroshenko, V.S. (2019). Komplektatsiia robotamy konveiernykh ta rotorno-konveiernykh linii lyvarnoho vyrobnytstva. Lyttia Ukrainy, (6), 21–23 [in Ukrainian].
4. Doroshenko, V.S. (2016). Lyttia po hazifikovanykh modeliakh z krystalizatsiieiu metalu pid tyskom. Lyvarne vyrobnytstvo, (1), 25–28 [in Ukrainian].
5. Kaliuzhnyi, P.B., Brodovyi, O.V., Doroshenko, V.S., & Neima, O.V. (2024). 3D-heneratsiia porystykh struktur dlia drukuvannia lyvarnykh modeli, shcho hazifikuiutsia v lyvarnii formi. Novi materialy i tekhnolohii v mashynobuduvanni, (5). https://doi.org/10.20535/2519-450x.5.2024.319032 [in Ukrainian].
6. Doroshenko, V.S. (2019). Kontseptsiia rotorno-konveiiernoho kompleksu dlia lytia po hazifikovanym modeliakh i termoobrabotky viddlivok. Metall i lytie Ukrainy, (1–2), 31–40. http://jnas.nbuv.gov.ua/article/UJRN-0001001769 [in Ukrainian].
7. Song, K.-T., Ou, S.-Q., Yang, C.-A., Sun, Y.-X., Kang, L.-R., Wang, Z.-Y., Wang, Y.-S., Lu, P.-C., Ko, C.-L., & Chen, Y.H. (2019). Scheduling and control of a wafer transfer robot for foundry equipment innovation competition. IFAC-PapersOnLine, 52(15), 627–632. https://doi.org/10.1016/j.ifacol.2019.11.654
8. Kaliuzhnyi, P.B., Shynskyi, O.Yi., & Doroshenko, V.S. (2024). Udoskonalennia stupinchastoi lyvnykovoi systemy dlia lytia za modeliami, shcho hazifikuiutsia. In Lytvo. Metalurhiia: Materialy XX Yuvileinoi Mizhnarodnoi naukovo-praktychnoi konferentsii, Kharkiv, 30 travnia 2024 (pp. 123–126). https://repository.kpi.kharkov.ua/handle/KhPI-Press/79750 [in Ukrainian].
9. Kaliuzhnyi, P., Doroshenko, V., & Neima, O. (2023). Casting of combined polymer patterns that are gasified. Casting Processes, 152(2), 49–55. https://doi.org/10.15407/plit2023.02.049
10. ABB Robotics AB. (2015). ABB Robotics Magazine. International customer magazine, (2). https://surl.li/wvzrjm
11. Yang, Y., Zheng, Y., Xu, Y., Yang, M., Boyang, B., & Liu, F. (2016). Robotic applications of foundry industry in China. In Proceedings of the 72nd World Foundry Congress, May 21–25, 2016, Nagoya. https://www.senkyo.co.jp/shishido/wfc2016/pdf/O-153.pdf
12. Lynch, K.M., & Park, F.C. (2017). Modern robotics: Mechanics, planning and control. Cambridge: Cambridge University Press. http://modernrobotics.org
13. Goldstein, H., Poole, C., & Safko, J. (2002). Classical mechanics. Addison-Wesley. https://surl.li/mrdcyr
14. Bertsekas, D. (2012). Dynamic programming and optimal control. Athena Scientific. https://surl.li/srkwtv
15. Batchelor, G.K. (2000). An introduction to fluid dynamics. Cambridge University Press. https://doi.org/10.1017/CBO978051180095
16. Heaton, I., Goodfellow, J., Ian, Y., Bengio, Y., & Courville, A. (2017). Deep learning. Genetic Programming and Evolvable Machines, 19(1–2), 305–307. https://doi.org/10.1007/s10710-017-9314-z
Citations
1. Doroshenko V., Yanchenko O. Improving the resource efficiency of foundry due to the assembly of conveyor and rotor-conveyor lines by robots. Modern Technology, Materials and Design in Construction. 2020. Vol. 27. № 2. P. 179–186. DOI: 10.31649/2311-1429-2019-2-179-186
2. Дорошенко В. С. Метод нейтралізації газів, що виділяються з ливарної форми при литві за газифікованими моделями. Ливарне виробництво. 2021. № 9. С. 8-14.
3. Дорошенко В.С. Комплектація роботами конвеєрних та роторно-конвеєрних ліній ливарного виробництва. Лиття України. 2019. № 6. С. 21-23.
4. Дорошенко, В.С. Лиття по газифікованих моделям з кристалізацією металу під тиском. Ливарне виробництво. 2016. № 1. С. 25-28.
5. Калюжний П. Б., Бродовий О. В., Дорошенко В. C., Нейма О. В. 3d-генерація пористих структур для друку ливарних моделей, що газифікуються в ливарній формі. Нові матеріали і технології в машинобудуванні (Праці Міжнародної науково-технічної конференції). 2024. № 5. DOI: 10.20535/2519-450x.5.2024.319032
6. Дорошенко В. С. Концепція роторно-конвейєрного комплексу для літа по газофікованим моделям і термообробки відливок. Металл и литье Украины. 2019. № 1-2. С. 31-40. URL: http://jnas.nbuv.gov.ua/article/UJRN-0001001769
7. Song K.-T., Ou S.-Q., Yang C.-A., Sun Y.-X., Kang L.-R., Wang Z.-Y., Wang Y.-S., Lu P.-C., Ko C.-L., Chen Y. H.. Scheduling and control of a wafer transfer robot for foundry equipment innovation competition. IFAC-PapersOnLine. 2019. Vol. 52. № 15. P. 627–632. DOI: 10.1016/j.ifacol.2019.11.654
8. Калюжний П.Б., Шинський O.Й., Дорошенко В.С. Удосконалення ступінчастої ливникової системи для лиття за моделями, що газифікуються. Литво. Металургія. 2024 : Матеріали XX Ювіл. Міжнар. науково-практ. конф., м. Харків, 30 трав. 2024 р. Харків. 2024. С. 123–126. URL: https://repository.kpi.kharkov.ua/handle/KhPI-Press/79750
9. Kaliuzhnyi P., Doroshenko V., Neima O. Casting of combined polymer patterns that are gasified. Casting processes. 2023. Vol. 152. № 2. P. 49–55. DOI: 10.15407/plit2023.02.049
10. ABB Robotics magazine. International customer magazine from ABB Robotics. 2015. № 2. Publisher: ABB Robotics AB. URL: https://surl.li/wvzrjm
11. Yang, Y., Zheng, Y., Xu, Y., Yang, M., Boyang, B., & Liu, F. Robotic applications of foundry industry in china. У Proceedings of the 72nd world foundry congress. 21-25th May 2016. Nagoya. URL: https://www.senkyo.co.jp/shishido/wfc2016/pdf/O-153.pdf
12. Lynch K. M., Park F. C. Modern robotics: mechanics, planning and control. Cambridge University Press, 2017. 544 p. URL: http://modernrobotics.org
13. Goldstein H., Poole C., Safko J., Classical Mechanics, Addison-Wesley, 2002. URL: https://surl.li/mrdcyr
14. Bertsekas D., Dynamic Programming and Optimal Control, Athena Scientific, 2012. URL: https://surl.li/srkwtv
15. Batchelor G. K., An Introduction to Fluid Dynamics, Cambridge University Press. 2000. DOI: 10.1017/CBO978051180095
16. Heaton I., Goodfellow J. Ian, Bengio Y., Courville A.: Deep learning. Genetic programming and evolvable machines. 2017. Vol. 19, № 1-2. P. 305–307. URL: DOI: 10.1007/s10710-017-9314-z