Numerical simulation of flexible fixture for multiaxis machining of bracket-type parts Vitalii Ivanov, Vitalii Kolos, Oksana Dynnyk International Journal on Interactive Design and Manufacturing, 2026 The relevance of the study is driven by current trends in the development of the machine-building industry and the increasing demands for production efficiency. Under conditions of market globalization, intensified competition, and the need to ensure high product quality, there is a pressing necessity to transition to flexible manufacturing systems capable of promptly adapting to changes in the product mix and production volumes. One of the key elements enabling such flexibility is machine tooling devices, which ensure precise positioning, clamping, and orientation of workpieces during machining processes. This research becomes particularly pertinent in the context of the widespread implementation of multifunctional CNC machine tools and the advancement of smart manufacturing concepts. Under these circumstances, the development of flexible tooling systems capable of efficiently machining complex-shaped parts while minimizing setup times and auxiliary costs is of critical importance. Therefore, the aim of this article is to develop a highly flexible fixture design for machining bracket-type parts, enabling the implementation of locating-and-clamping approaches. As a result of the study, a flexible fixture configuration for machining bracket-type components has been designed, offering full tool accessibility, which allows the reduction of non-productive time losses and enables its application in robotic manufacturing cells while ensuring operational flexibility. The obtained results allowed the evaluation of the proposed fixture design through stress–strain state analysis, determination of natural vibration frequencies, dynamic response analysis, and the construction of amplitude-frequency characteristics. To assess its performance, the proposed design was compared with a conventional fixture. Graphical Abstract
Material Homogeneity Criterion for Assessing Heterogeneous High-Strength Steel Joints with Austenitic Welds Yaroslav Kusyi, Vitalii Ivanov, Andriy Dzyubyk, Nazarii Kusen, Juraj Hajduk Machines, 2026 The modernization of global energy infrastructure within the Industry 5.0 framework requires the use of high-strength steels and reliable joining technologies to ensure safe, sustainable pipeline transport. This study focuses on the analysis of heterogeneous welded joints formed between high-strength alloy steel (34KhN2MA/EN 34CrNiMo6) and an austenitic welded seam (ER 307). While austenitic welds mitigate the risk of cold cracking, they introduce significant structural and mechanical heterogeneity. To address this, the research proposes and validates a material homogeneity criterion (MHC) derived from the LM-hardness methodology. By analyzing the statistical dispersion of macrohardness (HRC) through indicators such as the Weibull homogeneity coefficient (m) and the coefficient of variation (ν), the study establishes a quantitative approach to assess material degradation and structural uniformity across key weld zones. Results demonstrate that macrohardness profiling effectively distinguishes between structurally heterogeneous regions near the weld axis characterized by low homogeneity coefficients (m = 4.04 11.6%), and high technological damageability (D = 0.92 > 0.81, jD = 11.87 > 4.38) with pronounced step-like variation in macrohardness (HRC ∈ [12.6; 47]), on the one hand, and stabilized homogeneous zones in the base material, where m = 24.89 > 10, Am = 0.947 > 0.878, ν = 4.39% < 11.6%, D = 0.52 ⟶ 0, jD = 1.09 ⟶ 0, and characteristic range of HRC = 47–55, on the other hand. This methodology provides a robust, quasi-non-destructive tool for enhancing predictive maintenance, digital twins, and the overall integrity management of “smart” pipeline systems.
Simulation software for smart manufacturing: a review Vitalii Ivanov, Mykhailo Amelin, Bohdan Haidabrus Discover Applied Sciences, 2026 Modern manufacturing enterprises are faced with the challenges of increasing efficiency, adaptability, and integration of production processes in Industry 4.0. Discrete-event modeling and digital twins are key tools for optimizing production, planning, and decision-making. At the same time, choosing the optimal software remains difficult due to the various available platforms with different functionality. The article provides a systematic analysis of nine modern software programs for discrete-event modeling of production processes: (“FlexSim in Academia | FlexSim.” 2025. Accessed Aug 7. https://www.flexsim.com/academia/?utm ), Tecnomatix Plant Simulation, Visual Components, ProModel for AutoCAD, iGrafx, Arena Simulation, Delmia, simul8, and Simio. The assessment was carried out in five key categories: technical, analytical, organizational, economic, and educational, which allows for a comprehensive description of the functionality and scope of each software. The study's results confirm the leading role of FlexSim as a universal tool that combines powerful analytical capabilities, high scalability, integration with corporate systems, and advanced support for educational and research initiatives. A comparison of the indicators of other platforms demonstrates their strengths in individual aspects, but in general, emphasizes the need to consider the specifics of the tasks of enterprises and educational institutions when choosing software. The data obtained is of practical importance for modeling specialists, engineers, production managers, and teachers, contributing to increasing the efficiency of decision-making and the development of digital twins in the context of modern industrial technologies.
Optimization of Grinding and Wheel Parameters during External Cylindrical Longitudinal Machining of AISI 6150 Alloy Steel Based on Accuracy, Quality and Productivity Aleksandar Milošević, Vitalii Ivanov, Sanda Šimunović, Đorđe Vukelić Fme Transactions, 2026 In this research, a multi-objective optimization study was conducted on the external cylindrical longitudinal grinding process of AISI 6150 alloy steel. The input variables examined for their influence included wheel speed, workpiece speed, feed, total depth of cut, number of passes, wheel grain size, and wheel porosity. Experimental research was carried out using a custom experimental design based on the I-criterion of optimality. Dimensional deviation was selected to quantify accuracy, surface roughness was used for quality assessment, and material removal rate was employed to measure productivity. The dimensional deviation values ranged from 0.0046 to 0.0144 mm, surface roughness values were between 0.4301 and 3.766 μm, and the material removal rate ranged from 9.375 to 112.5 mm³. Using the experimental findings, an analysis was performed to define the impact of input variables on output variables, and regression equations were developed. The goal was to optimize accuracy, quality, and productivity simultaneously while varying the weighting coefficients in the objective function. The reliability of the model and the optimal values of the variables were validated through confirmation experiments. The obtained absolute errors were acceptable, measuring between 0.0004 to 0.001 mm for dimensional deviation and 0.0102 to 0.0245 μm for surface roughness.
Improving Agile Teams Effectiveness Through the Metrics Bohdan Haidabrus, Janis Grabis, Vitalii Ivanov, Evgeniy Druzhinin, Oleksandr Psarov 2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University Itms 2023 Proceedings, 2023
Automated Subsystem for Cutting Modes Calculations Vitalii Ivanov, Svitlana Vashchenko, Ivan Pavlenko, Michal Hatala, Vitalii Kolos, Vladyslav Andrusyshyn Eai Springer Innovations in Communication and Computing, 2023
Preface Lecture Notes in Mechanical Engineering, 2023
Synthesis of the Energy-Saving Dry Dual Clutch Control Mechanism Nikolay Sergienko, Pavel Kalinin, Ivan Pavlenko, Marek Ochowiak, Vitalii Ivanov, Anton Sergienko, Natalia Pavlova, Yevheniia Basova, Oksana Titarenko, Aleksandr Nazarov, Andżelika Krupińska, Magdalena Matuszak, Sylwia Włodarczak Applied Sciences Switzerland, 2023
Preface Maciej Capiński, , Núria Fagella, Michał Misiurewicz, Weixiao Shen, Benjamin Weiss, Krzysztof Ciepliński, , , , and Lecture Notes in Mechanical Engineering, 2022
Preface J. V. José, E. Saletan Lecture Notes in Mechanical Engineering, 2022
The Mathematical Model for the Secondary Breakup of Dropping Liquid Ivan Pavlenko, Vsevolod Sklabinskyi, Michał Doligalski, Marek Ochowiak, Marcin Mrugalski, Oleksandr Liaposhchenko, Maksym Skydanenko, Vitalii Ivanov, Sylwia Włodarczak, Szymon Woziwodzki, Izabela Kruszelnicka, Dobrochna Ginter-Kramarczyk, Radosław Olszewski, Bernard Michałek Energies, 2020
Parameter identification of hydro-mechanical processes using artificial intelligence systems International Journal of Mechatronics and Applied Mechanics, 2019
The use of virtual reality training application to increase the effectiveness of workshops in the field of lean manufacturing 4th International Conference of the Virtual and Augmented Reality in Education Vare 2018, 2018
Using the augmented reality for training engineering students 4th International Conference of the Virtual and Augmented Reality in Education Vare 2018, 2018
COMPOSITION, STRUCTURE, AND PROTECTIVE PROPERTIES OF COMPLEX COATINGS INVOLVING BORON AND SILICON, PRODUCED BY COMBINING THE PACK CEMENTATION METHOD WITH THE APPLICATION OF PRE … T Loskutova, V Ivanov, V Taran, N Kharchenko, M Karpets, L Krushynska, ... Surfaces and Interfaces, 109605 , 2026 2026
Towards more sustainable manufacturing technologies: shaping our tomorrow FJG Silva, V Ivanov, AMG Pinto, K Berladir, Y Denisenko, ... The International Journal of Advanced Manufacturing Technology, 1-1 , 2026 2026
The effect of outlet blade angle at the mean root diameter on the mixed inflow turbine MA Chelabi, J Pitel, Y Basova, S Dobrotvorskiy, V Ivanov Scientific Reports , 2026 2026
Material Homogeneity Criterion for Assessing Heterogeneous High-Strength Steel Joints with Austenitic Welds Y Kusyi, V Ivanov, A Dzyubyk, N Kusen, J Hajduk Machines , 2026 2026
Investigating the Influence of Cutting Insert Angles on Dimensional Deviation and Surface Roughness During Turning of Ti6Al4V Alloy D Vukelic, A Milosevic, G Simunovic, V Ivanov, Z Santosi, M Sokac, ... Tehnički vjesnik 33 (3), 1272-1281 , 2026 2026
Electrophysical properties and structural evolution under the influence of external factors of nanocrystalline molybdenum films as sensor electronics elements I Buryk, Y Shabelnyk, M Buryk, T Hovorun, K Berladir, L Odnodvorets, ... Next Materials 11, 101708 , 2026 2026
Numerical simulation of flexible fixture for multiaxis machining of bracket-type parts V Ivanov, V Kolos, O Dynnyk International Journal on Interactive Design and Manufacturing (IJIDeM), 1-17 , 2026 2026
Optimization of Grinding and Wheel Parameters during External Cylindrical Longitudinal Machining of AISI 6150 Alloy Steel Based on Accuracy, Quality and Productivity A Milosevic, V Ivanov, S Simunovic, D Vukelic 2026
Materials for the Automotive Industry K Berladir, Z Mitaľová, V Ivanov Springer Nature Switzerland, Imprint: Springer , 2026 2026 Citations: 3
Mechanisms of formation of different types of metal chips S Shvets, R Shvets, U Shvets, F Botko, I Dehtiarov, V Ivanov Advances in Science and Technology. Research Journal 19 (11), 317-328 , 2025 2025
Simulation software for smart manufacturing: a review V Ivanov, M Amelin, B Haidabrus Discover Applied Sciences , 2025 2025 Citations: 1
Non-destructive evaluation of quality parameters of Al-Si workpieces after milling Y Kusyi, V Ivanov, S Korniy, B Datsko, M Hatala The International Journal of Advanced Manufacturing Technology, 1-23 , 2025 2025 Citations: 3
Addressing manufacturing challenges of joint-type forks production through technological assurance V Ivanov, M Amelin, D Vukelic, M Hatala The International Journal of Advanced Manufacturing Technology, 1-17 , 2025 2025
Biocomposites K Berladir, Z Mitaľová, V Ivanov Materials for the Automotive Industry, 135-147 , 2025 2025
Aluminum and Its Alloys K Berladir, Z Mitaľová, V Ivanov Materials for the Automotive Industry, 55-67 , 2025 2025
Copper and Its Alloys K Berladir, Z Mitaľová, V Ivanov Materials for the Automotive Industry, 89-100 , 2025 2025
Rubber and Rubber Materials K Berladir, Z Mitaľová, V Ivanov Materials for the Automotive Industry, 157-168 , 2025 2025 Citations: 1
Smart Materials K Berladir, Z Mitaľová, V Ivanov Materials for the Automotive Industry, 169-184 , 2025 2025
Titanium and Its Alloys K Berladir, Z Mitaľová, V Ivanov Materials for the Automotive Industry, 101-110 , 2025 2025 Citations: 1
Cast Iron K Berladir, Z Mitaľová, V Ivanov Materials for the Automotive Industry, 41-54 , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
An overview of robot applications in automotive industry M Bartoš, V Bulej, M Bohušík, J Stanček, V Ivanov, P Macek Transportation Research Procedia 55, 837-844 , 2021 2021 Citations: 129
A simulation study of Industry 4.0 factories based on the ontology on flexibility with using FlexSimr software S Luscinski, VO Ivanov Production Engineering Committee of the Polish Academy of Sciences, Polish … , 2020 2020 Citations: 72
Scientific and methodological approach for the identification of mathematical models of mechanical systems by using artificial neural networks I Pavlenko, J Trojanowska, V Ivanov, O Liaposhchenko International Conference on Innovation, Engineering and Entrepreneurship … , 2018 2018 Citations: 71
Increasing of equipment efficiency by intensification of technological processes A Fesenko, Y Basova, V Ivanov, M Ivanova, F Yevsiukova, M Gasanov Periodica Polytechnica Mechanical Engineering 63 (1), 67-73 , 2019 2019 Citations: 69
Application of artificial neural network for identification of bearing stiffness characteristics in rotor dynamics analysis I Pavlenko, V Simonovskiy, V Ivanov, J Zajac, J Pitel Design, simulation, manufacturing: the innovation exchange, 325-335 , 2018 2018 Citations: 67
Parameter identification of cutting forces in crankshaft grinding using artificial neural networks I Pavlenko, M Saga, I Kuric, A Kotliar, Y Basova, J Trojanowska, V Ivanov Materials 13 (23), 5357 , 2020 2020 Citations: 66
Management of the grain supply chain during the conflict period: case study Ukraine O Pavlenko, D Muzylyov, V Ivanov, M Bartoszuk, J Jozwik Acta logistica 10 (3), 393-402 , 2023 2023 Citations: 64
Process-oriented approach to fixture design V Ivanov Design, Simulation, Manufacturing: The Innovation Exchange, 42-50 , 2018 2018 Citations: 62
Information Support of the Computer-aided Fixture Design System V Ivanov, S Vashchenko, YK Rong Proceedings of the 12th International Conference on ICT in Education … , 2016 2016 Citations: 61
Numerical simulation of the system “fixture–workpiece” for lever machining V Ivanov, D Mital, V Karpus, I Dehtiarov, J Zajac, I Pavlenko, M Hatala The International Journal of Advanced Manufacturing Technology 91 (1), 79-90 , 2017 2017 Citations: 60
Determination of contact points between workpiece and fixture elements as a tool for augmented reality in fixture design V Ivanov, I Pavlenko, O Liaposhchenko, O Gusak, V Pavlenko Wireless Networks 27 (3), 1657-1664 , 2021 2021 Citations: 59
Ensuring the economic efficiency of enterprises by multi-criteria selection of the optimal manufacturing process A Kotliar, Y Basova, VO Ivanov, O Murzabulatova, S Vasyltsova, ... Production Engineering Committee of the Polish Academy of Sciences, Polish … , 2020 2020 Citations: 55
Technology for complex parts machining in multiproduct manufacturing V Ivanov, I Dehtiarov, I Pavlenko, I Kosov, M Kosov Management and Production Engineering Review 10 (2) , 2019 2019 Citations: 55
Choice of the optimal configuration of modular reusable fixtures VE Karpus, VA Ivanov Russian Engineering Research 32 (3), 213-219 , 2012 2012 Citations: 54
Mathematical modeling of operating process and technological features for designing the vortex type liquid-vapor jet apparatus I Merzliakov, I Pavlenko, O Chekh, S Sharapov, V Ivanov Design, Simulation, Manufacturing: The Innovation Exchange, 613-622 , 2019 2019 Citations: 53
Ensuring vibration reliability of turbopump units using artificial neural networks I Pavlenko, V Ivanov, I Kuric, O Gusak, O Liaposhchenko International Scientific-Technical Conference MANUFACTURING, 165-175 , 2019 2019 Citations: 52
Estimation of the reliability of automatic axial-balancing devices for multistage centrifugal pumps I Pavlenko, J Trojanowska, O Gusak, V Ivanov, J Pitel, V Pavlenko Periodica Polytechnica Mechanical Engineering 63 (1), 52-56 , 2019 2019 Citations: 52
The use of virtual reality training application to increase the effectiveness of workshops in the field of lean manufacturing P Buń, J Trojanowska, V Ivanov, I Pavlenko 4th International Conference of the Virtual and Augmented Reality in … , 2018 2018 Citations: 52
Using the augmented reality for training engineering students V Ivanov, I Pavlenko, P Bun, Y Zuban, D Samokhvalov, J Trojanowska 4th International Conference of the Virtual and Augmented Reality in … , 2018 2018 Citations: 51
Experimental diagnostic research of fixture V Ivanov, I Dehtiarov, Y Denysenko, N Malovana, N Martynova Diagnostyka 19 , 2018 2018 Citations: 51