Yevgen Tsegelnyk is an Associate Professor at the Department of Automation and Computer-Integrated Technologies of the O.M. Beketov National University of Urban Economy in Kharkiv (Ukraine). He received his Ph.D. degree in Aerospace Engineering from National Aerospace University “Kharkiv Aviation Institute” (Ukraine) in 2010. He has more than 15 years of international experience in research and development. He had an international traineeship as Post Doctorate at the Czech Technical University in Prague (Czech Republic) in 2014. His research interests cover innovation technologies, including the methods of manufacturing for the digital industry, processing with intense energy fluxes (technologies using plasma, laser, and detonation sources), additive and subtractive manufacturing technologies, applied computing techniques.
EDUCATION
National Aerospace University “Kharkiv Aviation Institute”
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, Industrial and Manufacturing Engineering, Control and Systems Engineering, Renewable Energy, Sustainability and the Environment
53
Scopus Publications
732
Scholar Citations
16
Scholar h-index
29
Scholar i10-index
Scopus Publications
Preface Lecture Notes in Networks and Systems, 2026
INTERACTIVE PROGRAMMING OF FILAMENT WINDING OPERATIONS ON CNC MACHINES International Journal of Mechatronics and Applied Mechanics, 2026 Filament winding is a widely used manufacturing technology for producing fiberreinforced polymer (FRP) pipes and tubular structures with tailored mechanical properties.The quality and repeatability of the winding process strongly depend on the accuracy of trajectory generation, smooth motion control, and correct coordination of machine axes, especially when using low-cost CNC winding machines with a limited number of controlled axes.This paper presents an approach to interactive programming of filament winding operations directly on CNC machines, without the use of external CAM systems.Mathematical models for helical and circumferential winding layers, as well as transition trajectories between layers, are developed for a two-axis winding scheme.The proposed models ensure uniform fiber distribution, controlled overlapping, and smooth motion in reversal zones by applying circular interpolation instead of conventional linear interpolation.An approach to the development of a dedicated software application is presented, and an interactive tool integrated into the CNC programming environment is described.The application enables layer-by-layer design of winding structures, automatic calculation of winding parameters, and direct generation of CNC programs.Experimental validation based on quantitative wall thickness measurements was carried out on a two-axis winding machine during the manufacture of glass-fiber composite pipes.The results demonstrate stable winding patterns, uniform wall thickness, and reliable reproducibility of the programmed trajectories, confirming the practical applicability of the proposed interactive CNC-based programming approach.
Review of Physics-Informed Neural Networks: Challenges in Loss Function Design and Geometric Integration Sergiy Plankovskyy, Yevgen Tsegelnyk, Nataliia Shyshko, Igor Litvinchev, Tetyana Romanova, José Manuel Velarde Cantú Mathematics, 2025 Physics-Informed Neural Networks (PINNs) represent a transformative approach to solving partial differential equation (PDE)-based boundary value problems by embedding physical laws into the learning process, addressing challenges such as non-physical solutions and data scarcity, which are inherent in traditional neural networks. This review analyzes critical challenges in PINN development, focusing on loss function design, geometric information integration, and their application in engineering modeling. We explore advanced strategies for constructing loss functions—including adaptive weighting, energy-based, and variational formulations—that enhance optimization stability and ensure physical consistency across multiscale and multiphysics problems. We emphasize geometry-aware learning through analytical representations—signed distance functions (SDFs), phi-functions, and R-functions—with complementary strengths: SDFs enable precise local boundary enforcement, whereas phi/R capture global multi-body constraints in irregular domains; in practice, hybrid use is effective for engineering problems. We also examine adaptive collocation sampling, domain decomposition, and hard-constraint mechanisms for boundary conditions to improve convergence and accuracy and discuss integration with commercial CAE via hybrid schemes that couple PINNs with classical solvers (e.g., FEM) to boost efficiency and reliability. Finally, we consider emerging paradigms—Physics-Informed Kolmogorov–Arnold Networks (PIKANs) and operator-learning frameworks (DeepONet, Fourier Neural Operator)—and outline open directions in standardized benchmarks, computational scalability, and multiphysics/multi-fidelity modeling for digital twins and design optimization.
Heat recovery systems from sewage: The current state and development prospects Olga Arsenyeva, Ihor Biletskyi, Sergiy Plankovskyy, Petro Kapustenko, Yevgen Tsegelnyk, Natalia Teliura Sustainable Development and the Evolution of Civil Engineering, 2025 Renewable energy, crucial amid global warming, emphasizes waste heat utilization in cities. Industrial waste heat and domestic wastewater offer significant potential. In the UK, up to 6.1% of heat needs could be met by wastewater recovery. Domestic systems can save up to 50% of hot water energy. Recent research focuses on improving heat exchanger efficiency for household applications. However, larger-scale systems lack attention. Solutions for extracting heat from sewer collectors are limited. New technologies aim to restore collectors and recover heat efficiently. Plastic pipes offer promising rehabilitation methods, ensuring prolonged collector life. The proposed chapter will detail efficient heat exchangers and innovative sewer heat recovery methods, addressing existing limitations and future research prospects.
AXISYMMETRIC PROBLEM OF SMOOTHING THE SURFACE OF A VISCOUS LIQUID BY SURFACE TENSION FORCES Vitalii Myntiuk, Olga Shypul, Oleh Tryfonov, Yevgen Tsegelnyk Radioelectronic and Computer Systems, 2025 This study investigates an analytical solution to the problem of the surface levelling of viscous liquids under the influence of surface tension forces, focusing on the smoothing of plastic surfaces subjected to thermal energy treatment. This study aims to extend Orchard’s formula to axisymmetric surface irregularities and develop an analytical model for predicting levelling time, thereby ensuring efficient process control in thermal treatment applications. The tasks included deriving an analytical solution for axisymmetric levelling, validating it against numerical simulations in LS-DYNA, and incorporating the viscosity variation across the liquid layer. The methods involved analytical formulation and numerical simulation of surface evolution considering different initial surface geometries and viscosity distributions. Validation against numerical results demonstrated high accuracy for moderate and thick liquid layers ( ) and initial surface amplitudes up to 40% of the characteristic radius. Following validation, the model was applied to estimate levelling times for various surface configurations while maintaining simplicity while improving the predictive capabilities. Results showed that the extended formula effectively describes surface smoothing dynamics, including the cases with thickness-dependent viscosity, providing explicit expressions for levelling time. These findings enable precise control of heat input during thermal energy treatment, thereby optimizing the surface quality. In conclusion, the proposed analytical solutions offer a practical tool for surface levelling analysis, expanding the applicability of Orchard’s approach to more complex geometries and viscosity variations. In future work, we will focus on experimental validation and refinements to enhance the accuracy in industrial applications.
Machine learning and HPC in computer-aided design of electric machines Vladyslav Pliuhin, Yevgen Tsegelnyk, Maria Sukhonos, Ihor Biletskyi, Sergiy Plankovskyy, Vitaliy Tietieriev Integrating Machine Learning into HPC Based Simulations and Analytics, 2024 The current approach to designing electric machines is carried out using a 'cascade' algorithm, wherein sequential calculations are performed according to formulas derived from electromagnetic theory. However, this approach is tailored to each specific type of electric machine, making modification of projects difficult and not always feasible. One promising solution to address this issue is to leverage HPC (High-Performance Computing) calculations in conjunction with machine learning capabilities. This chapter examines established methodologies for designing and optimizing electric machines, ultimately advocating for the integration of machine learning and modern optimization algorithms.
Sparsest packing of two-dimensional objects Tatiana Romanova, Alexander Pankratov, Igor Litvinchev, Sergiy Plankovskyy, Yevgen Tsegelnyk, Olga Shypul International Journal of Production Research, 2021
Circular layout in thermal deburring Sergiy Plankovskyy, Olga Shypul, Yevgen Tsegelnyk, Alexander Pankratov, Tatiana Romanova, Igor Litvinchev Advances in Intelligent Systems and Computing, 2021
NTERACTIVE PROGRAMMING OF FILAMENT WINDING OPERATIONS ON CNC MACHINES. O Kivirenko, V Voronko, I Vasyliev, S Terzin, S Kostenko, I Dolzhenko, ... International Journal of Mechatronics & Applied Mechanics, 332 , 2026 2026
Application of machine learning for infrastructure reconstruction programs management I Khudiakov, V Pliuhin, S Plankovskyy, Y Tsegelnyk arXiv preprint arXiv:2511.20916 , 2025 2025
Virtual high voltage lab: gamified learning in a safe 3d environment V Pliuhin, Y Tsegelnyk, M Sukhonos, I Biletskyi, S Plankovskyy, ... arXiv preprint arXiv:2511.20919 , 2025 2025
Review of physics-informed neural networks: Challenges in loss function design and geometric integration S Plankovskyy, Y Tsegelnyk, N Shyshko, I Litvinchev, T Romanova, ... Mathematics 13 (20), 3289 , 2025 2025 Citations: 19
Heat Recovery Systems From Sewage: The Current State and Development Prospects O Arsenyeva, I Biletskyi, S Plankovskyy, P Kapustenko, Y Tsegelnyk, ... Sustainable Development and the Evolution of Civil Engineering, 69-106 , 2025 2025 Citations: 2
Prediction and compensation of motion differential characteristics influence on position error in CNC machine tools V Kombarov, P Fojtu, M Sulitka, Y Aksonov, J Sveda, Y Tsegelnyk, ... The International Journal of Advanced Manufacturing Technology 137, 5951–5981 , 2025 2025 Citations: 7
Дослідження розповсюдження тріщини в зонах концентраторів напружень під дією термоімпульсного навантаження в деталях з ливарними дефектами ОВ Шипуль, ВБ Минтюк, ДА Ткаченко, ОА Павленко, ДА Брега, ... Авіаційно-космічна техніка і технологія 2, 55-66 , 2025 2025
Axisymmetric problem of smoothing the surface of a viscous liquid by surface tension forces V Myntiuk, O Shypul, O Tryfonov, Y Tsegelnyk Radioelectronic and Computer Systems 2025 (1), 113-125 , 2025 2025 Citations: 1
Комп'ютерна програма "Комп'ютерна модель режимів роботи мережі електропостачання" ВЄ Плюгін, СІ Планковський, ІО Худяков, ОМ Петренко, ЄО Аксьонов, ... 2025
Комп'ютерна програма "Комп'ютерна програма з прогнозування споживання електричної енергії від різних джерел енергії на базі машинного навчання" ВЄ Плюгін, СІ Планковський, ІО Худяков, ОМ Петренко, ЄВ Цегельник 2025
Machine learning and HPC in computer-aided design of electric machines V Pliuhin, Y Tsegelnyk, M Sukhonos, I Biletskyi, S Plankovskyy, ... Integrating machine learning into HPC-based simulations and analytics, 145-196 , 2025 2025 Citations: 3
Modernization of Hybrid Power Supply Networks Using Hydrogen Generators V Pliuhin, S Plankovskyy, Y Tsegelnyk, V Tietieriev, T Sakhoshko International Journal of Mechatronics and Applied Mechanics 2024 (18), 274-282 , 2024 2024 Citations: 1
Efficient Part's Shape and Its Placement in Closed Space under Thermal Energy Treatment S Plankovskyy, Y Tsegelnyk, O Shypul, T Romanova, V Kombarov International Journal of Mechatronics and Applied Mechanics 2024 (18), 264-273 , 2024 2024 Citations: 3
Ammonia condensation in the horizontal and vertical straight inner-grooved tubes and annuli V Ruzaikin, I Lukashov, A Breus, Y Tsegelnyk, S Plankovskyy International Journal of Heat and Mass Transfer 233, 126031 , 2024 2024 Citations: 3
Asymptotic Analysis of Nonlinear Modes of Multi-Walled Nanotubes K Avramov, S Plankovskyy, Y Tsegelnyk, V Kombarov, M Avramov 2024 IEEE 5th KhPI Week on Advanced Technology (KhPIWeek), 1-6 , 2024 2024
Digital Twins of Different Types Electrical Machines V Pliuhin, Y Tsegelnyk, O Slovikovskyi Lighting Engineering & Power Engineering 63 (2), 35-45 , 2024 2024 Citations: 2
Implementation features of local and remote technical objects digital twins V Pliuhin, M Sukhonos, I Biletskyi, S Plankovskyy, Y Tsegelnyk IOP Conference Series: Earth and Environmental Science 1376 (1), 012036 , 2024 2024 Citations: 11
Packing Like-Jammed Spheres in a Cylindrical Layer S Plankovsky, Y Tsegelnyk, G Yaskov, T Romanova, P Stetsyuk International Conference on Smart Technologies in Urban Engineering, 336-344 , 2024 2024
Investigation of Velocity Estimation Methods for CNC Feedback Loops Y Aksonov, V Kombarov, Y Tsegelnyk, D Dovhan, D Kostomakha International Conference on Smart Technologies in Urban Engineering, 117-127 , 2024 2024
Комп'ютерна програма" Система автоматизованого проектування асинхронного двигуна" ВЄ Плюгін, ВО Тєтєрєв, ЄВ Цегельник, СІ Планковський, ІВ Білецький 2024
MOST CITED SCHOLAR PUBLICATIONS
Sparsest balanced packing of irregular 3D objects in a cylindrical container T Romanova, Y Stoyan, A Pankratov, I Litvinchev, S Plankovskyy, ... European Journal of Operational Research 291 (1), 84-100 , 2021 2021 Citations: 50
Numerical control of fiberglass pipe bends manufacturing V Kombarov, Y Kryzhyvets, I Biletskyi, Y Tsegelnyk, Y Aksonov, ... 2021 IEEE 2nd KhPI Week on Advanced Technology (KhPIWeek), 357-362 , 2021 2021 Citations: 44
Sparsest packing of two-dimensional objects T Romanova, A Pankratov, I Litvinchev, S Plankovskyy, Y Tsegelnyk, ... International Journal of Production Research 59 (13), 3900-3915 , 2021 2021 Citations: 41
Advanced thermal energy method for finishing precision parts S Plankovskyy, V Popov, O Shypul, Y Tsegelnyk, O Tryfonov, D Brega Advanced Machining and Finishing, 527-575 , 2021 2021 Citations: 39
Powder Mixtures Analysis for Laser Cladding Using OpenCV Library D Kritskiy, O Pohudina, M Kovalevskyi, Y Tsegelnyk, V Kombarov Integrated Computer Technologies in Mechanical Engineering – 2021, 924-937 , 2022 2022 Citations: 34
Numerical Control of Machining Parts from Aluminum Alloys with Sticking Minimization V Kombarov, V Sorokin, Y Tsegelnyk, S Plankovskyy, Y Aksonov, O Fojtů International Journal of Mechatronics and Applied Mechanics, 209-216 , 2021 2021 Citations: 34
Cutting irregular objects from the rectangular metal sheet S Plankovskyy, Y Tsegelnyk, O Shypul, A Pankratov, T Romanova Integrated Computer Technologies in Mechanical Engineering, 150-157 , 2020 2020 Citations: 29
Visualization and analysis of technological systems experimental operating results Y Aksonov, V Kombarov, Y Tsegelnyk, S Plankovskyy, O Fojtù, ... 2021 IEEE 16th International Conference on Computer Sciences and Information … , 2021 2021 Citations: 24
Spectral Methods Application in Problems of the Thin-walled Structures Deformation D Tkachenko, Y Tsegelnyk, S Myntiuk, V Myntiuk Journal of Applied and Computational Mechanics 8 (2), 641-654 , 2022 2022 Citations: 23
Review of physics-informed neural networks: Challenges in loss function design and geometric integration S Plankovskyy, Y Tsegelnyk, N Shyshko, I Litvinchev, T Romanova, ... Mathematics 13 (20), 3289 , 2025 2025 Citations: 19
Analytical methods for determining the static and dynamic behavior of thin-walled structures during machining S Plankovskyy, V Myntiuk, Y Tsegelnyk, S Zadorozhniy, V Kombarov Mathematical Modeling and Simulation of Systems (MODS'2020), 82-91 , 2021 2021 Citations: 19
Implementation of induction motor speed and torque control system with reduced order model in ANSYS Twin Builder V Pliuhin, Y Tsegelnyk, S Plankovskyy, O Aksonov, V Kombarov Lecture Notes in Networks and Systems 762, 514-531 , 2023 2023 Citations: 18
Determination of Massive Rotary Electric Machines Parameters in ANSYS RMxprt and ANSYS Maxwell V Pliuhin, M Zablodskiy, M Sukhonos, Y Tsegelnyk, L Piddubna Lecture Notes in Networks and Systems 536, 189-201 , 2023 2023 Citations: 18
S-Shape Feedrate Scheduling Method with Smoothly-Limited Jerk in Cyber-Physical Systems V Kombarov, V Sorokin, Y Tsegelnyk, S Plankovskyy, Y Aksonov, O Fojtů International Conference on Reliable Systems Engineering (ICoRSE) - 2021, 54-68 , 2022 2022 Citations: 18
Investigation of the Required Discreteness of Interpolation Movement Parameters in Cyber-physical Systems V Kombarov, Y Tsegelnyk, S Plankovskyy, Y Aksonov, Y Kryzhyvets Periodica Polytechnica Mechanical Engineering 66 (1), 1-9 , 2022 2022 Citations: 17
Amplification of heat transfer by shock waves for Thermal Energy Method S Plankovskyy, O Shypul, Y Tsegelnyk, A Pankratov, T Romanova Integrated Computer Technologies in Mechanical Engineering - 2020, 577-587 , 2021 2021 Citations: 17
Simulation of surface heating for arbitrary shape’s moving bodies/sources by using R-functions S Plankovskyy, O Shypul, Y Tsegelnyk, O Tryfonov, I Golovin Acta Polytechnica 56 (6), 472-477 , 2016 2016 Citations: 16
Circular layout in thermal deburring S Plankovskyy, O Shypul, Y Tsegelnyk, A Pankratov, T Romanova, ... Mathematical Modeling and Simulation of Systems (MODS'2020), 111-120 , 2021 2021 Citations: 14
Современные методы утилизации полимерных композиционных материалов СИ Планковский, ВО Гарин, ЕВ Цегельник Открытые информационные и компьютерные интегрированные технологии 51, 186-194 , 2011 2011 Citations: 13
Design and simulation of a servo-drive motor using ANSYS Electromagnetics V Pliuhin, O Aksonov, Y Tsegelnyk, S Plankovskyy, V Kombarov, ... Lighting Engineering & Power Engineering 60 (3), 112-123 , 2021 2021 Citations: 12