A Structured Methodology for Synthesizing Parameters and Architecture of Robotic Technological Systems in the Digital Transformation of SME Engineering Production
Keywords:
Robotic Automation, SME Engineering, Process Synthesis, Digital Transformation, Technological Systems, Workplace Design, Industry 4.0.Abstract
INTRODUCTION: Robotic automation has become a key driver of digital transformation in the engineering sector, especially for small and medium-sized enterprises (SMEs), which face increasing demands for flexibility, efficiency, and cost optimization. However, most classical automation frameworks do not address the structural and economic limitations specific to SMEs. Despite recent advances in modular automation, a gap remains in methodologies tailored to low-volume, high-mix environments typical of SMEs.
OBJECTIVES: This paper aims to develop a structured methodology for synthesizing the parameters and architecture of robotic technological systems adapted to the production realities of SME engineering environments. The goal is to balance automation effectiveness with practical investment constraints.
METHODS: The proposed approach integrates a multi-level automation model with production system analysis, considering object-specific constraints, part characteristics, and process parameters. The methodology was validated through an expert- and data-driven case study of a Ukrainian SME engaged in serial plastic part machining. Functional-cost analysis and feasibility modeling were used to evaluate automation options. In addition, investment-efficiency mapping was introduced to support strategic planning of implementation phases.
RESULTS: The implementation of the proposed system, based on a six-axis robotic manipulator with digital control and vacuum clamping devices, led to a 50% reduction in auxiliary processing time, improved consistency, and reduced labor intensity. The workplace-level automation enabled flexible part handling without the need for major structural changes or high capital investment. The system demonstrated high adaptability to part variations and required minimal operator intervention.
CONCLUSION: The developed methodology provides a scalable and economically viable path to robotic automation for SMEs. It supports gradual implementation and can be further enhanced by integrating artificial intelligence tools for decision-making during system design and optimization. This structured framework contributes to the digital resilience of SME manufacturing and aligns with Industry 4.0 principles.
References
[1] Groover MP. Automation production systems and computer-integrated manufacturing. 4th ed. Upper Saddle River: Pearson Education; 2015.
[2] Groover MP. Fundamentals of modern manufacturing: materials, processes and systems. 4th ed. Hoboken: John Wiley & Sons; 2010.
[3] Tao F, Zhang M, Liu Y, Nee AYC. Digital twin in industry: State-of-the-art. IEEE Trans Ind Inform. 2021;17(6):4024–4034.
[4] ElMaraghy H, Schuh G, ElMaraghy W, Piller F, Schönsleben P, Tseng M, Bernard A. Product variety management. CIRP Ann. 2021;70(2):495–518.
[5] Mourtzis D, Angelopoulos J. A digital twin-based approach for the development of an automated robotic system for flexible manufacturing. Robot Comput Integr Manuf. 2021;68:102062.
[6] Yakimov OV. Technology of automated machine building. Odesa: [Publisher unspecified]; 2005. [in Ukrainian].
[7] Yakovenko I, Permyakov A, Prihodko O, Basova Y, Ivanova M. Structural optimization of technological layout of modular machine tools. In: Tonkonogyi V, et al., editors. Advanced Manufacturing Processes. Proceedings of InterPartner 2019; 2019; Ukraine. Cham: Springer; 2020. p. 352–363. https://doi.org/10.1007/978-3-030-40724-7_36
[8] Yakovenko I, Permyakov A, Naboka O, Prihodko O, Havryliuk Y. Parametric optimization of technological layout of modular machine tools. In: Ivanov V, Trojanowska J, Pavlenko I, Zajac J, Peraković D, editors. Advances in Design, Simulation and Manufacturing III. Proceedings of DSMIE 2020; 2020; Ukraine. Cham: Springer; 2020. p. 85–93. https://doi.org/10.1007/978-3-030-50794-7_9
[9] Yakovenko I, Permyakov A, Dobrotvorskiy S, Basova Ye, Kotliar A, Zinchenko A. Prospects for the development of process equipment in aggregate-modular design for sustainable mechanical engineering. Int J Mechatronics Appl Mech. 2023;13:145–156. https://doi.org/10.17683/ijomam/issue13.18
[10] Swift K, Booker J. Manufacturing Process Selection Handbook. Amsterdam: Elsevier Science; 2013.
[11] Grznár P, Burganová N, Mozol Š, Mozolová LA. Comprehensive digital model approach for adaptive manufacturing systems. Appl Sci. 2023;13(10706).
[12] Brecher C, et al. editors. Model-based controlling approaches for manufacturing processes. In: Internet of Production. Interdisciplinary Excellence Accelerator Series. Cham: Springer; 2023.
[13] Shevchenko VV, Tymchyk GS. Basics of automation of technological processes. Electronic network edition. Kyiv: [Publisher unspecified]; 2023. [in Ukrainian].
[14] Mulyar YI, Repinskyi SV. Automation of production in mechanical engineering. Part II. Vinnytsia: [Publisher unspecified]; 2019. [in Ukrainian].
[15] Gunko YuL, Fedorus YuV. Automation of production processes. Lutsk: Lutsk National Technical University; 2015. [in Ukrainian].
[16] Grigoryuk EN, Bulkin VV. Problems of automation and management principles information flow in manufacturing. IOP Conf Ser Mater Sci Eng. 2017;221. https://doi.org/10.1088/1757-899X/221/1/012017
[17] Subedi D, Tyapin I, Hovland G. Review on modeling and control of flexible link manipulators. Model Ident Control. 2020;41(3):141–163. https://doi.org/10.1177/0959651816642099
[18] FutureBridge. Adoption of artificial intelligence in industrial machinery: analysis. March 2020. Available from: https://www.futurebridge.com/blog/artificialintelligence-in-industrial-machinery/
[19] Basova Ye, Dobrotvorskiy S, Balog M, Iakovets A, Chelabi M, Zinchenko A. Increasing SME supply chain resilience in the face of rapidly changing demand with 3D model visualization. Int J Mechatronics Appl Mech. 2023;14:35–47. https://doi.org/10.17683/ijomam/issue14.5
[20] Sevic M, Keller P. Model smart factory using principles of INDUSTRY 4.0. MM Sci J. 2021;1:4238–4243. https://doi.org/10.17973/MMSJ.2021_03_2020067
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ihor Yakovenko, Yevheniia Basova, Alexander Permyakov, Andrii Pokhil, Victor Sotnychenko, Luís Freitas
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
