Actual Tasks in Creating Digital Twins for Precise Electrochemical Machining
DOI:
https://doi.org/10.33042/2079-424X.2024.63.1.02Keywords:
Electrochemical Machining, Multi-physics Simulation, Digital Twin, Industry 4.0Abstract
The development and application of manufacturing processes digital twins is an established trend in the development of precision machining methods, which is inherent in Industry 4.0. Digital twins are most actively implemented in additive manufacturing and for non-deformation finishing processes: laser, electrical discharge, and electrochemical. The main advantage of this approach is the ability not only to understand and control the process but also to manage it in real time while simultaneously monitoring the condition of the equipment. Electrochemical machining (ECM) is stands out among non-deformation methods. ECM combines high material removal rates with the absence of tool wear and thermal effects on the processed material. The most promising process is pulsed electrochemical machining (PECM), in which the cathode carries oscillatory motion and high-density electrical pulses are applied when it is near the lower dead center. This ensures high productivity and processing accuracy, as well as improved conditions for electrolyte renewal in the machining zone during the cathode's return stroke. Due to the complexity and interrelated of the processes involved in PECM, multi-physical models are used to create digital twins. Based on the review of existing models for PECM, tasks have been formulated for creating DT of machining processes for complex-shaped details, where two-dimensional models, which most modern research is based on, cannot be applied. The most important tasks are the design of cathodes, optimization of electrolyte supply and integration of electrochemical machining process digital twins and equipment for its implementation. The possibilities of creating such integrated digital twins using available software on the market are determined.
References
Guo, Y., Klink, A., Bartolo, P., & Guo, W.G. (2023). Digital twins for electro-physical, chemical, and photonic processes. CIRP Annals, 72(2), 593–619. https://doi.org/10.1016/j.cirp.2023.05.007
Rosen, R., Von Wichert, G., Lo, G., & Bettenhausen, K.D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnline, 48(3), 567–572. https://doi.org/10.1016/j.ifacol.2015.06.141
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2014). Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans-actions on Industrial Informatics, 11(3), 812–820. https://doi.org/10.1109/TII.2014.2349359
Jiang, J., & Ma, Y. (2020). Path planning strategies to optimize accuracy, quality, build time and material use in additive manufacturing: a review. Micromachines, 11(7), 633. https://doi.org/10.3390/mi11070633
Rahman, M.A., Saleh, T., Jahan, M.P., McGarry, C., Chaudhari, A., Huang, R., ... & Shakur, M.S. (2023). Re-view of intelligence for additive and subtractive manu-facturing: current status and future prospects. Microm-achines, 14(3), 508. https://doi.org/10.3390/mi14030508
Xu, Z., & Wang, Y. (2021). Electrochemical ma-chining of complex components of aero-engines: devel-opments, trends, and technological advances. Chinese Journal of Aeronautics, 34(2), 28–53. https://doi.org/10.1016/j.cja.2019.09.016
Datta, M., & Landolt, D. (1981). Electrochemical machining under pulsed current conditions. Electrochimica Acta, 26(7), 899–907. https://doi.org/10.1016/0013-4686(81)85053-0
Loebel, S., Zinecker, M., Steinert, P., & Schubert, A. (2022). Transient simulation of electrochemical machin-ing processes for manufacturing of surface structures in high‐strength materials. Engineering Reports, 4(7-8), e12360. https://doi.org/10.1002/eng2.12360
Klocke, F., Heidemanns, L., Zeis, M., & Klink, A. (2018). A novel modeling approach for the simulation of precise electrochemical machining (PECM) with pulsed current and oscillating cathode. Procedia CIRP, 68, 499–504. https://doi.org/10.1016/j.procir.2017.12.081
Chen, Y., Jiang, L., Fang, M., & Zhang, J. (2020). Multi-time scale simulation of pulse electrochemical machining process with multi-physical model. The Inter-national Journal of Advanced Manufacturing Technology, 110, 2203–2210. https://doi.org/10.1007/s00170-020-06017-y
Kozak, J., Dabrowski, L., Lubkowski, K., Rozenek, M., & Sławiński, R. (2000). CAE-ECM system for electro-chemical technology of parts and tools. Journal of Materi-als Processing Technology, 107(1-3), 293–299. https://doi.org/10.1016/S0924-0136(00)00685-3
Schaarschmidt, I., Hackert-Oschätzchen, M., Meichsner, G., Zinecker, M., & Schubert, A. (2019). Im-plementation of the machine tool-specific current and voltage control characteristics in multiphysics simula-tion of electrochemical precision machining. Procedia CIRP, 82, 237–242. https://doi.org/10.1016/j.procir.2019.04.142
Narayanan, O. H., Hinduja, S., & Noble, C.F. (1986). Design of tools for electrochemical machining by the boundary element method. Proceedings of the Institu-tion of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 200(3), 195–205. https://doi.org/10.1243/PIME_PROC_1986_200_115_02
Riggs, J.B., Muller, R.H., & Tobias, C.W. (1981). Prediction of work piece geometry in electrochemical cavity sinking. Electrochimica Acta, 26(8), 961–969. https://doi.org/10.1016/0013-4686(81)85064-5
Deconinck, D., Van Damme, S., & Deconinck, J. (2012). A temperature dependent multi-ion model for time accurate numerical simulation of the electrochemi-cal machining process. Part I: Theoretical basis. Electro-chimica Acta, 60, 321–328. https://doi.org/10.1016/j.electacta.2011.11.070
Deconinck, D., Van Damme, S., & Deconinck, J. (2012). A temperature dependent multi-ion model for time accurate numerical simulation of the electrochemi-cal machining process. Part II: Numerical simulation. Electrochimica Acta, 69, 120–127. https://doi.org/10.1016/j.electacta.2012.02.079
Schaarschmidt, I., Zinecker, M., Hackert-Oschätzchen, M., Meichsner, G., & Schubert, A. (2017). Multiscale multiphysics simulation of a pulsed electro-chemical machining process with oscillating cathode for microstructuring of impact extrusion punches. Procedia CIRP, 58, 257–262. https://doi.org/10.1016/j.procir.2017.04.005
Smets, N., Van Damme, S., De Wilde, D., Weyns, G., & Deconinck, J. (2007). Time averaged temperature calculations in pulse electrochemical machining. Part I: Theoretical basis. Journal of Applied Electrochemistry, 37, 1345–1355. https://doi.org/10.1007/s10800-007-9394-1
Smets, N., Van Damme, S., De Wilde, D., Weyns, G., & Deconinck, J. (2008). Time averaged temperature calculations in pulse electrochemical machining, Part II: Numerical simulation. Journal of Applied Electrochemistry, 38, 551–560. https://doi.org/10.1007/s10800-007-9472-4
Chen, Y., Fang, M., & Jiang, L. (2017). Multiphysics simulation of the material removal process in pulse electrochemical machining (PECM). The International Journal of Advanced Manufacturing Technology, 91, 2455–2464. https://doi.org/10.1007/s00170-016-9899-z
Hotoiu, L., & Deconinck, J. (2013). Time-efficient simulations of nano-pulsed electrochemical micro-machining. Procedia CIRP, 6, 469–474. https://doi.org/10.1016/j.procir.2013.03.107
Schaarschmidt, I., Meichsner, G., Zinecker, M., Ha-ckert-Oschätzchen, M., & Schubert, A. (2017). Multiscale model of the PECM with oscillating cathode for external geometries using a virtual switch. In Proceedings of the 2017 COMSOL Conference in Rotterdam (pp. 1–7). COMSOL.
Deutsche Institut für Normung (2018). Methode zur Bestimmung von Prozesseingangsgrößen für das elektrochemische Präzisionsabtragen - Anforderungen, Kriterien, Festlegungen (DIN SPEC 91399:2018-12). https://doi.org/10.31030/3007935
Thielecke, A., Meichsner, G., & Hackert-Oschätzchen, M. (2023). Digital twin for the determina-tion of process input variables for electrochemical preci-sion machining according to DIN SPEC 91399. Materials Research Proceedings, 28, 1653–1662. https://doi.org/10.21741/9781644902479-178
Smets, N., Van Damme, S., De Wilde, D., Weyns, G., & Deconinck, J. (2007). Calculation of temperature transients in pulse electrochemical machining (PECM). Journal of Applied Electrochemistry, 37, 315–324. https://doi.org/10.1007/s10800-006-9259-z
Schaarschmidt, I., Hackert-Oschätzchen, M., Meichsner, G., Zinecker, M., & Schubert, A. (2019). Im-plementation of the machine tool-specific current and voltage control characteristics in multiphysics simula-tion of electrochemical precision machining. Procedia CIRP, 82, 237–242. https://doi.org/10.1016/j.procir.2019.04.142
Menter, F., Hüppe, A., Matyushenko, A., & Kol-mogorov, D. (2021). An overview of hybrid RANS–LES models developed for industrial CFD. Applied Sciences, 11(6), 2459. https://doi.org/10.3390/app11062459
Spille-Kohoff, A., Schulze, R., Meichsner, G., Ha-ckert-Oschätzchen, M., & Busan, S. (2016). Optimized design of electrochemical machining processes by a combination of 3D simulation and rapid prototyping of cathodes. In Proceedings of the International Symposium on Electrochemical Machining Technology (INSECT 2015) (pp. 1–10). Shaker Verlag.
Wang, Y., Xu, Z., Liu, J., Zhang, A., Xu, Z., Meng, D., & Zhao, J. (2021). Study on flow field of electrochemi-cal machining for large size blade. International Journal of Mechanical Sciences, 190, 106018. https://doi.org/10.1016/j.ijmecsci.2020.106018
Speidel, A., Bisterov, I., Saxena, K.K., Zubayr, M., Reynaerts, D., Natsu, W., & Clare, A.T. (2022). Electro-chemical jet manufacturing technology: From fundamen-tals to application. International Journal of Machine Tools and Manufacture, 180, 103931. https://doi.org/10.1016/j.ijmachtools.2022.103931
Kombarov, V., Sorokin, V., Tsegelnyk, Y., Plankovskyy, S., & Aksonov, Y. (2023). S-shape feedrate profile with smoothly-limited jerk for threading move-ments synchronization in CNC machining. In O. Ar-senyeva, T. Romanova, M. Sukhonos, & Y. Tsegelnyk, Y. (Eds.), Smart Technologies in Urban Engineering. LNNS, vol. 536 (pp. 593–605). Springer. https://doi.org/10.1007/978-3-031-20141-7_54
Aksonov, Y., Kombarov, V., Fojtů, O., Sorokin, V., & Kryzhyvets, Y. (2019). Investigation of processes in high-speed equipment using CNC capabilities. MM Sci-ence Journal, 2019(November), 3271–3276. https://doi.org/10.17973/MMSJ.2019_11_2019081
Aksonov, Y., Kombarov, V., Tsegelnyk, Y., Plankovskyy, S., Fojtù, O., & Piddubna, L. (2021). Visuali-zation and analysis of technological systems experi-mental operating results. In 2021 IEEE 16th International Conference on Computer Sciences and Information Technolo-gies (CSIT) (Vol. 2, pp. 141-146). IEEE. https://doi.org/10.1109/CSIT52700.2021.9648592
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Lighting Engineering & Power Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors that are published in this journal agree with the following terms:
- The authors reserve the right of authorship of his work and pass to the journal the right of first publication of this work is licensed under a Creative Commons Attribution License, which allows others to freely distribute published work with reference to authors of original works and works first published in this journal.
- The authors have the right to enter into a separate additional agreement for non-exclusive distribution of work in the form in which it was published in the magazine (for example, to place work in electronic storage agencies or publish as part of the monograph), providing the reference to the first publication in this journal.
- Journal policy allows and encourages the placement by the authors on the Internet (eg, in storage facilities or personal websites) the manuscript of the works before the submission of the manuscript to the editor as well as during its editorial processing, as it contributes to productive scientific discussion and has a positive impact on efficiency and dynamics citing published work (see. The Effect of Open Access).