Integration of magnetic resonance processing (MRP) into the manufacturing processes of critical machine components
DOI:
https://doi.org/10.37142/2076-2151/2025-1(54)147Keywords:
magnetic resonance processing; pressure forming; mathematical modeling; adaptive manufacturing; intelligent control systems; Industry 4.0.Abstract
Kovalevskyy S., Semichasnova N. Integration of magnetic resonance processing (MRP) into the manufacturing processes of critical machine components.
This paper presents a comprehensive approach for integrating magnetic resonance Processing (MRP) into both hot and cold stamping processes of critical machine components. Twelve analytical models have been developed, each describing a specific stage of interaction between the alternating magnetic field and the workpiece or tooling: from nucleation and domain-structure modification to residual-stress relaxation, dislocation-density reduction, and minimization of microfracture probability. It is demonstrated that the contactless application of MRP allows for localized, controlled formation of dislocation clusters, elimination of tensorial hardness anisotropy, and homogenization of phase heterogeneities without the need for additional thermal treatment. Experimental and analytical results show a marked improvement in the stability of cutting process parameters: dynamic tool loads are reduced, the repeatability of dimensional and geometric characteristics of finished parts is enhanced, and tooling life is extended by slowing the accumulation of fatigue damage between cycles. By applying MRP before or after stamping, an optimal balance between work-hardening and its relaxation is achieved, increasing material ductility without conventional heat treatment and lowering energy consumption. The scientific novelty lies in combining these analytical models with the paradigm of Industry 4.0 cyber-physical manufacturing systems. MRP emerges as a key element of adaptive production: real-time sensor data enable targeted micromodification of material properties, while digital models forecast their evolution under process loads. This opens the way for self-regulating production chains with closed-loop feedback that integrate plastic deformation, structural modeling, and digital control. The practical significance of this work is confirmed by its potential to substantially reduce overall production costs and energy consumption through fewer thermal cycles, while also enhancing environmental safety. The proposed methodology provides a solid foundation for the development of intelligent manufacturing systems and advanced surface-engineering technologies in mechanical engineering.
References
He X., Welo T., Ma J. In-process monitoring strategies and methods in metal forming: A selective review. Journal of Manufacturing Processes. 2025. 138, pp. 100–128. DOI: https://doi.org/10.1016/j.jmapro.2025.02.011.
La Monaca A., Murray J.W., Liao Z., Speidel A., Robles-Linares J.A., Axinte D. A., Hardy M. C., Clare A. T. Surface integrity in metal machining. Part II: Functional performance. International Journal of Machine Tools and Manufacture. 2021. 164. Article 103718. DOI: https://doi.org/10.1016/j.ijmachtools.2021.103718.
Ma J., Tang X., Hou Y., Li H., Lin J., Fu M. W. Defects in metal-forming: formation mechanism, prediction and avoidance // International Journal of Machine Tools and Manufacture. 2025. 207. Article 104268. DOI: https://doi.org/10.1016/j.ijmachtools.2025.104268.
Zhao J., Jiang Z. Thermomechanical processing of advanced high strength steels. Progress in Materials Science. 2018. 94, pp. 174–242. DOI: https://doi.org/10.1016/j.pmatsci.2018.01.006.
Kovalevskyy S. V., Kovalevska O. S., Sidyuk D. M. Modeling the effects of magnetic resonance processing of materials and its experimental validation. Bulletin of the National Technical University “KhPI,” Series: Technologies in Mechanical Engineering: 2024. 2 (10), pp. 9–14. DOI: https://doi.org/10.20998/2079-004X.2024.2(10).02. (in Ukrainian).
Mourtzis D., Angelopoulos J., Panopoulos N. 3.02 – Industry 4.0 and smart manufacturing. Comprehensive Materials Processing. 2nd ed. / ed. by Saleem Hashmi. Elsevier. 2024, pp. 14–38. DOI: https://doi.org/10.1016/B978-0-323-96020-5.00010-8.
Chen J., Zhang Z., Wang L., Tang D., Cai Q., Chen K. Self-adaptive production scheduling for discrete manufacturing workshop using multi-agent cyber physical system. Engineering Applications of Artificial Intelligence. 2025. 150. Article 110638. DOI: https://doi.org/10.1016/j.engappai.2025.110638.
Kosse S., Betker V., Hagedorn P., König M., Schmidt T. A semantic digital twin for the dynamic scheduling of Industry 4.0-based production of precast concrete elements. Advanced Engineering Informatics. 2024. 62. Part B. Article 102677. DOI: https://doi.org/10.1016/j.aei.2024.102677.
Nourizadeh H., Setayesh Nazar M. Optimal day-ahead scheduling and reconfiguration of active distribution systems considering energy hubs, residential demand response aggregators, and electric vehicle parking lot aggregators. Computers and Electrical Engineering. 2025. 123, Part C. Article 110227. DOI: https://doi.org/10.1016/j.compeleceng.2025.110227 .
Heik D., Bahrpeyma F., Reichelt D. Study on the application of single-agent and multi-agent reinforcement learning to dynamic scheduling in manufacturing environments with growing complexity: Case study on the synthesis of an industrial IoT Test Bed. Journal of Manufacturing Systems. 2024. 77, pp. 525–557. DOI: https://doi.org/10.1016/j.jmsy.2024.09.019.
