DOI: https://doi.org/10.32515/2664-262X.2025.12(43).1.326-332
Features of Building Software Simulation to Optimize the Efficiency and Reliability of Automated Production Lines Using AI Methods
About the Authors
Serhii Kovalov, PhD in Pedagogy (Candidate of Pedagogical Sciences), Associate Professor of the Department of Higher Mathematics and Physics, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0002-3922-8697, e-mail: kovalyovserggr@ukr.net
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Andrii Hrynkiv, Senior Researcher, PhD (Candidate of Technical Sciences), Senior Lecturer of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4478-1940, e-mail: AVGrinkiv@gmail.com.
Yurii Kovalov, Associate Professor, PhD in Technical Sciences (Candidate of Technical Sciences), Associate Professor of the Department of Materials Science and Foundry Production, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-1729-2033, e-mail: yukovalyov@ukr.net
Abstract
This article explores a methodological approach to integrating artificial intelligence (AI) into automated production lines through the development of computer-based simulations. As a case study, the research focuses on an experimental assembly line for agricultural drones, designed to serve as a testbed for intelligent optimization strategies. The simulation is constructed using object-oriented programming principles, enabling modularity, scalability, and architectural clarity. Each component of the production line—conveyors, manipulators, quality control nodes, and drone modules—is modeled as an independent object with defined behaviors and interaction protocols.
A key objective of the study is to replace selected elements of the simulation architecture with AI-driven agents capable of learning and adapting to dynamic production conditions. These agents implement reinforcement learning algorithms and heuristic decision-making strategies aimed at improving the overall efficiency, reliability, and responsiveness of the manufacturing process. The simulation environment supports both graphical implementations, using modern game engines such as Unity, and console-based models developed in Python or C++, allowing for comparative analysis of performance, flexibility, and integration potential.
The concept of digital twins is central to the proposed framework, providing a virtual mirror of the physical production system that can be used for predictive analytics, resource optimization, and real-time decision support. The study highlights the advantages of simulation-based AI integration, including reduced development risk, accelerated prototyping, and enhanced system transparency. Minor limitations, such as abstraction gaps and computational overhead, are acknowledged and discussed.
Overall, the research contributes to the architectural foundations of intelligent manufacturing systems by demonstrating how simulation environments can serve as platforms for testing, training, and deploying AI agents. The findings are relevant to developers of industrial automation, digital twin frameworks, and adaptive control systems seeking to enhance production line performance through intelligent technology.
Keywords
artificial intelligence (AI), automated production lines, computer simulation, intelligent agents, efficiency optimization, digital twins, simulation model architecture, integration of AI into production
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References
1. Sajadieh, S.M.M., & Noh, S. D. (2025). From simulation to autonomy: Reviews of the integration of artificial intelligence and digital twins. International Journal of Precision Engineering and Manufacturing- Green Technology, 12, 1597–1628. https://doi.org/10.1007/s40684-025-00750-z
2. Lee, H., & Yang, H. (2023). Digital twinning and optimization of manufacturing process flows. Journal of Manufacturing Science and Engineering, 145 (11), 111008. https://doi.org/10.1115/1.4063234
3. Li, J., & Yang, S. X. (2025). Digital twins to embodied artificial intelligence: Review and perspective. Intelligent Robotics, 5(1), 202–227. https://doi.org/10.20517/ir.2025.1
4. Jeong, Y. (2023). Digitalization in production logistics: How AI, digital twins, and simulation are driving the shift from model-based to data-driven approaches. International Journal of Precision Engineering and Manufacturing-Smart Technology, 1(2), 187–200. https://doi.org/10.57062/ijpem-st.2023.0052
5. Aulin, V. V., Grynkiv, A. V., Lysenko, S. V., & Holub, D. V. (2019). Synergetics of improving machine reliability using Markov process models. In *Proceedings the Development of Structures and Technical Service of Agricultural Machinery and Tools: materialy pyatoi vseukrainskoi naukovo-praktychnoyi konferentsiyi (pp. 242–245). Zhytomyr: Zhytomyr Agricultural College [in Ukrainian]
6. Aulin, V. V. (Ed.). (2020). Methodological foundations for the design and operation of intelligent transport and production systems. Kropyvnytskyi: Vydavets Lysenko V. F. [in Ukrainian]
7. Ohira, K., & Ohira, T. (2025). Solving a delay differential equation through the Fourier transform. Physics Letters A, 531, 130138. https://doi.org/10.1016/j.physleta.2024.130138
8. Derbas, M., Frömel-Frybort, S., Möhring, H.-C., & Riegler, M. (2025). Accelerated singular spectrum analysis and machine learning to investigate wood machining acoustics. Mechanical Systems and Signal Processing, 223, 111879. https://doi.org/10.1016/j.ymssp.2024.111879
9. Aulin, V. V., Kovalov, S. H., Grynkiv, A. V., & Varvarov, V. V. (2024). Optimization algorithm for reliability and efficiency of production equipment using artificial intelligence methods. Central Ukrainian Scientific Bulletin. Technical Sciences, 10 (41), Part I, 60–67. [in Ukrainian]
10. Kovalov, S. H., & Kovalov, Yu. H. (2024). Features of implementing artificial neural network models in hardware. Science and Technology Today. Series: Engineering, 6(34), 854–866. [in Ukrainian]. https://doi.org/10.52058/2786-6025-2024-6(34)
11. Barillaro, L. (2024). Deep learning platforms: Keras. In Reference Module in Life Sciences. https://doi.org/10.1016/B978-0-323-95502-7.00092-0
12. Pandya, S., & Ghayvat, H. (2021). Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence. Advanced Engineering Informatics, 47, 101238. https://doi.org/10.1016/j.aei.2020.101238
13. Aulin, V. V., Kovalov, S. H., Grynkiv, A. V., & Varvarov, V. V. (2024). Improving reliability and efficiency of production lines using artificial intelligence and acoustic signal monitoring. Central Ukrainian Scientific Bulletin. Technical Sciences, 10 (41), Part II. 142–151 [in Ukrainian]. https://doi.org/10.32515/2664- 262X.2024.10(41).2.142-151
14. Kovalov, S. H., Aulin, V. V., Grynkiv, A. V., & Kovalov, Y. H. (2025). Modeling the stochastic state matrix of a production line to optimize its operational reliability using reinforcement learning. Central Ukrainian Scientific Bulletin. Technical Sciences, 11 (42), Part II. 195–203. https://doi.org/10.32515/2664- 262X.2025.11(42).2.195-203
15. Kovalov, S. H. (2025). Production time optimization using reinforcement learning as a case of improving automated production line efficiency. Central Ukrainian Scientific Bulletin. Technical Sciences, 11 (42), Part I. 198–205 [in Ukrainian]. https://doi.org/10.32515/2664-262X.2025.11(42).1.198-205
Citations
1. Sajadieh S. M. M., Noh S. D. From Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins. International Journal of Precision Engineering and Manufacturing-Green Technology. 2025. Vol. 12. P. 1597–1628. DOI: 10.1007/s40684-025-00750-z.
2. Lee H., Yang H. Digital Twinning and Optimization of Manufacturing Process Flows. Journal of Manufacturing Science and Engineering. 2023. Vol. 145, № 11. Article 111008. DOI: 10.1115/1.4063234.
3. Li J., Yang S. X. Digital Twins to Embodied Artificial Intelligence: Review and Perspective. Intelligent Robotics. 2025. Vol. 5, № 1. P. 202–227. DOI: 10.20517/ir.2025.1.
4. Jeong Y. Digitalization in Production Logistics: How AI, Digital Twins, and Simulation are Driving the Shift from Model-Based to Data-Driven Approaches. International Journal of Precision Engineering and Manufacturing-Smart Technology. 2023. Vol. 1, № 2. P. 187–200. DOI: 10.57062/ijpem-st.2023.0052.
5. Аулін В. В., Гриньків А. В., Лисенко С. В., Голуб Д. В. Синергетика підвищення надійності машин використанням моделей Марківських процесів. Перспективи і тенденції розвитку конструкцій та технічного сервісу сільськогосподарських машин і знарядь: матеріали V Всеукр. наук.-практ. конф., 9- 10 квіт. 2019 р. Житомир: Житомирський агротехнічний коледж, 2019. С. 242–245.
6. Методологічні основи проектування та функціонування інтелектуальних транспортних і виробничих систем / за ред. В. В. Ауліна. Кропивницький: Видавець Лисенко В. Ф., 2020. 428 с.
7. Ohira K., Ohira T. Solving a Delay Differential Equation Through the Fourier Transform. Physics Letters A. 2025. Vol. 531. Article 130138. DOI: 10.1016/j.physleta.2024.130138.
8. Derbas M., Frömel-Frybort S., Möhring H.-C., Riegler M. Accelerated Singular Spectrum Analysis and Machine Learning to Investigate Wood Machining Acoustics. Mechanical Systems and Signal Processing. 2025. Vol. 223. Article 111879. DOI: 10.1016/j.ymssp.2024.111879.
9. Аулін В. В., Ковальов С. Г., Гриньків А. В., Варваров В. В. Алгоритм оптимізації надійності функціонування та ефективності використання виробничого обладнання методами штучного інтелекту. Центральноукраїнський науковий вісник. Технічні науки. 2024. Вип. 10(41), ч. I. С. 60–67.
10. Ковальов С. Г., Ковальов Ю. Г. Особливості реалізації моделі штучної нейронної мережі апаратними засобами. Наука і техніка сьогодні. Серія «Техніка». 2024. № 6(34). С. 854–866. DOI: 10.52058/2786- 6025-2024-6(34).
11. Barillaro L. Deep Learning Platforms: Keras. Reference Module in Life Sciences. 2024. January. DOI: 10.1016/B978-0-323-95502-7.00092-0.
12. Pandya S., Ghayvat H. Ambient Acoustic Event Assistive Framework for Identification, Detection, and Recognition of Unknown Acoustic Events of a Residence. Advanced Engineering Informatics. 2021. Vol. 47. Article 101238. DOI: 10.1016/j.aei.2020.101238.
13. Аулін В. В., Ковальов С. Г., Гриньків А. В., Варваров В. В. Підвищення надійності та ефективності експлуатації виробничих ліній методами штучного інтелекту, використовуючи моніторинг акустичних сигналів. Центральноукраїнський науковий вісник. Технічні науки. 2024. Вип. 10(41), ч. ІІ. С. 142–151. DOI: 10.32515/2664-262X.2024.10(41).2.142-151.
14. Kovalov S. G., Aulin V. V., Grynkiv A. V., Kovalov Yu. G. Modeling the Stochastic State Matrix of a Production Line for Optimize its Operational Reliability Using Reinforcement Learning. Central Ukrainian Scientific Bulletin. Technical Sciences. 2025. Issue 11(42), Part II. P. 195–203. DOI: 10.32515/2664- 262X.2025.11(42).2.195-203.
15. Ковальов С. Г. Оптимізація часу виробництва за допомогою методу навчання з підкріпленням як частинний випадок підвищення ефективності автоматизованих виробничих ліній. Центральноукраїнський науковий вісник. Технічні науки. 2025. Вип. 11(42), Ч. I. С. 198–205. DOI: 10.32515/2664-262X.2025.11(42).1.198-205.
Copyright (©) 2025, Serhii Kovalov, Viktor Aulin, Andrii Grynkiv, Yurii Kovalov
Features of Building Software Simulation to Optimize the Efficiency and Reliability of Automated Production Lines Using AI Methods
About the Authors
Serhii Kovalov, PhD in Pedagogy (Candidate of Pedagogical Sciences), Associate Professor of the Department of Higher Mathematics and Physics, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0002-3922-8697, e-mail: kovalyovserggr@ukr.net
Viktor Aulin, Professor, Doctor of Technical Sciences, Professor of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0003-2737-120X, e-mail: aulinvv@gmail.com
Andrii Hrynkiv, Senior Researcher, PhD (Candidate of Technical Sciences), Senior Lecturer of the Department of Machinery Operation and Repair, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-4478-1940, e-mail: AVGrinkiv@gmail.com.
Yurii Kovalov, Associate Professor, PhD in Technical Sciences (Candidate of Technical Sciences), Associate Professor of the Department of Materials Science and Foundry Production, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0000-0002-1729-2033, e-mail: yukovalyov@ukr.net
Abstract
Keywords
Full Text:
PDFReferences
1. Sajadieh, S.M.M., & Noh, S. D. (2025). From simulation to autonomy: Reviews of the integration of artificial intelligence and digital twins. International Journal of Precision Engineering and Manufacturing- Green Technology, 12, 1597–1628. https://doi.org/10.1007/s40684-025-00750-z
2. Lee, H., & Yang, H. (2023). Digital twinning and optimization of manufacturing process flows. Journal of Manufacturing Science and Engineering, 145 (11), 111008. https://doi.org/10.1115/1.4063234
3. Li, J., & Yang, S. X. (2025). Digital twins to embodied artificial intelligence: Review and perspective. Intelligent Robotics, 5(1), 202–227. https://doi.org/10.20517/ir.2025.1
4. Jeong, Y. (2023). Digitalization in production logistics: How AI, digital twins, and simulation are driving the shift from model-based to data-driven approaches. International Journal of Precision Engineering and Manufacturing-Smart Technology, 1(2), 187–200. https://doi.org/10.57062/ijpem-st.2023.0052
5. Aulin, V. V., Grynkiv, A. V., Lysenko, S. V., & Holub, D. V. (2019). Synergetics of improving machine reliability using Markov process models. In *Proceedings the Development of Structures and Technical Service of Agricultural Machinery and Tools: materialy pyatoi vseukrainskoi naukovo-praktychnoyi konferentsiyi (pp. 242–245). Zhytomyr: Zhytomyr Agricultural College [in Ukrainian]
6. Aulin, V. V. (Ed.). (2020). Methodological foundations for the design and operation of intelligent transport and production systems. Kropyvnytskyi: Vydavets Lysenko V. F. [in Ukrainian]
7. Ohira, K., & Ohira, T. (2025). Solving a delay differential equation through the Fourier transform. Physics Letters A, 531, 130138. https://doi.org/10.1016/j.physleta.2024.130138
8. Derbas, M., Frömel-Frybort, S., Möhring, H.-C., & Riegler, M. (2025). Accelerated singular spectrum analysis and machine learning to investigate wood machining acoustics. Mechanical Systems and Signal Processing, 223, 111879. https://doi.org/10.1016/j.ymssp.2024.111879
9. Aulin, V. V., Kovalov, S. H., Grynkiv, A. V., & Varvarov, V. V. (2024). Optimization algorithm for reliability and efficiency of production equipment using artificial intelligence methods. Central Ukrainian Scientific Bulletin. Technical Sciences, 10 (41), Part I, 60–67. [in Ukrainian]
10. Kovalov, S. H., & Kovalov, Yu. H. (2024). Features of implementing artificial neural network models in hardware. Science and Technology Today. Series: Engineering, 6(34), 854–866. [in Ukrainian]. https://doi.org/10.52058/2786-6025-2024-6(34)
11. Barillaro, L. (2024). Deep learning platforms: Keras. In Reference Module in Life Sciences. https://doi.org/10.1016/B978-0-323-95502-7.00092-0
12. Pandya, S., & Ghayvat, H. (2021). Ambient acoustic event assistive framework for identification, detection, and recognition of unknown acoustic events of a residence. Advanced Engineering Informatics, 47, 101238. https://doi.org/10.1016/j.aei.2020.101238
13. Aulin, V. V., Kovalov, S. H., Grynkiv, A. V., & Varvarov, V. V. (2024). Improving reliability and efficiency of production lines using artificial intelligence and acoustic signal monitoring. Central Ukrainian Scientific Bulletin. Technical Sciences, 10 (41), Part II. 142–151 [in Ukrainian]. https://doi.org/10.32515/2664- 262X.2024.10(41).2.142-151
14. Kovalov, S. H., Aulin, V. V., Grynkiv, A. V., & Kovalov, Y. H. (2025). Modeling the stochastic state matrix of a production line to optimize its operational reliability using reinforcement learning. Central Ukrainian Scientific Bulletin. Technical Sciences, 11 (42), Part II. 195–203. https://doi.org/10.32515/2664- 262X.2025.11(42).2.195-203
15. Kovalov, S. H. (2025). Production time optimization using reinforcement learning as a case of improving automated production line efficiency. Central Ukrainian Scientific Bulletin. Technical Sciences, 11 (42), Part I. 198–205 [in Ukrainian]. https://doi.org/10.32515/2664-262X.2025.11(42).1.198-205
Citations
1. Sajadieh S. M. M., Noh S. D. From Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins. International Journal of Precision Engineering and Manufacturing-Green Technology. 2025. Vol. 12. P. 1597–1628. DOI: 10.1007/s40684-025-00750-z.
2. Lee H., Yang H. Digital Twinning and Optimization of Manufacturing Process Flows. Journal of Manufacturing Science and Engineering. 2023. Vol. 145, № 11. Article 111008. DOI: 10.1115/1.4063234.
3. Li J., Yang S. X. Digital Twins to Embodied Artificial Intelligence: Review and Perspective. Intelligent Robotics. 2025. Vol. 5, № 1. P. 202–227. DOI: 10.20517/ir.2025.1.
4. Jeong Y. Digitalization in Production Logistics: How AI, Digital Twins, and Simulation are Driving the Shift from Model-Based to Data-Driven Approaches. International Journal of Precision Engineering and Manufacturing-Smart Technology. 2023. Vol. 1, № 2. P. 187–200. DOI: 10.57062/ijpem-st.2023.0052.
5. Аулін В. В., Гриньків А. В., Лисенко С. В., Голуб Д. В. Синергетика підвищення надійності машин використанням моделей Марківських процесів. Перспективи і тенденції розвитку конструкцій та технічного сервісу сільськогосподарських машин і знарядь: матеріали V Всеукр. наук.-практ. конф., 9- 10 квіт. 2019 р. Житомир: Житомирський агротехнічний коледж, 2019. С. 242–245.
6. Методологічні основи проектування та функціонування інтелектуальних транспортних і виробничих систем / за ред. В. В. Ауліна. Кропивницький: Видавець Лисенко В. Ф., 2020. 428 с.
7. Ohira K., Ohira T. Solving a Delay Differential Equation Through the Fourier Transform. Physics Letters A. 2025. Vol. 531. Article 130138. DOI: 10.1016/j.physleta.2024.130138.
8. Derbas M., Frömel-Frybort S., Möhring H.-C., Riegler M. Accelerated Singular Spectrum Analysis and Machine Learning to Investigate Wood Machining Acoustics. Mechanical Systems and Signal Processing. 2025. Vol. 223. Article 111879. DOI: 10.1016/j.ymssp.2024.111879.
9. Аулін В. В., Ковальов С. Г., Гриньків А. В., Варваров В. В. Алгоритм оптимізації надійності функціонування та ефективності використання виробничого обладнання методами штучного інтелекту. Центральноукраїнський науковий вісник. Технічні науки. 2024. Вип. 10(41), ч. I. С. 60–67.
10. Ковальов С. Г., Ковальов Ю. Г. Особливості реалізації моделі штучної нейронної мережі апаратними засобами. Наука і техніка сьогодні. Серія «Техніка». 2024. № 6(34). С. 854–866. DOI: 10.52058/2786- 6025-2024-6(34).
11. Barillaro L. Deep Learning Platforms: Keras. Reference Module in Life Sciences. 2024. January. DOI: 10.1016/B978-0-323-95502-7.00092-0.
12. Pandya S., Ghayvat H. Ambient Acoustic Event Assistive Framework for Identification, Detection, and Recognition of Unknown Acoustic Events of a Residence. Advanced Engineering Informatics. 2021. Vol. 47. Article 101238. DOI: 10.1016/j.aei.2020.101238.
13. Аулін В. В., Ковальов С. Г., Гриньків А. В., Варваров В. В. Підвищення надійності та ефективності експлуатації виробничих ліній методами штучного інтелекту, використовуючи моніторинг акустичних сигналів. Центральноукраїнський науковий вісник. Технічні науки. 2024. Вип. 10(41), ч. ІІ. С. 142–151. DOI: 10.32515/2664-262X.2024.10(41).2.142-151.
14. Kovalov S. G., Aulin V. V., Grynkiv A. V., Kovalov Yu. G. Modeling the Stochastic State Matrix of a Production Line for Optimize its Operational Reliability Using Reinforcement Learning. Central Ukrainian Scientific Bulletin. Technical Sciences. 2025. Issue 11(42), Part II. P. 195–203. DOI: 10.32515/2664- 262X.2025.11(42).2.195-203.
15. Ковальов С. Г. Оптимізація часу виробництва за допомогою методу навчання з підкріпленням як частинний випадок підвищення ефективності автоматизованих виробничих ліній. Центральноукраїнський науковий вісник. Технічні науки. 2025. Вип. 11(42), Ч. I. С. 198–205. DOI: 10.32515/2664-262X.2025.11(42).1.198-205.
Copyright (©) 2025, Serhii Kovalov, Viktor Aulin, Andrii Grynkiv, Yurii Kovalov