DOI: 10.5937/jaes0-28489
This is an open access article distributed under the CC BY 4.0
Volume 19 article 809 pages: 424-431
The article presents the development of a method for selecting the operating mode of the induction brazing process
based on intelligent methods. The use of intelligent methods is due to the presence of uncertain conditions caused
by the complexity of the initial setting of the technological parameters of the induction brazing process, the error of
measuring instruments, and the human factor. The use of smart methods will make it possible to reduce the impact
of negative factors, remove uncertainty, and adequately perform the initial set of technological parameters for the
induction brazing process. Artificial neural networks, the fuzzy controller and the neural fuzzy controller have been
chosen as the smart methods in this work. The article gives a brief overview of the above methods, provides a rationale
for the choice of intelligent methods, and also compares their effectiveness. Based on the results of the experimental
efficiency check, the most suitable method for determining the choice of induction brazing process operation
is proposed.
This work was supported by the Ministry of Science and
Higher Education of the Russian Federation (State Contract
No. FEFE-2020-0013)
1. Li, Y., Chen, C., Yi, R., Ouyang, Y. (2020). Special brazing and soldering. Journal of Manufacturing Processes, vol. 60, 608-635, DOI: 10.1016/j.jmapro. 2020.10.049
2. Lanin, V. L. (2018). Sizing up the efficiency of induction heating systems for soldering electronic modules. Surface Engineering and Applied Electrochemistry, vol. 54, no. 4, 401-406, DOI: 10.3103/ S1068375518040105
3. Drienovsky, M., Michalcova, E., Pekarcíkova, M., Palcut, M., Frolek, L., Gogola, P., Gömöry, F. (2018). Induction brazing of coated conductor high-temperature superconducting tapes with lead-free solder alloys. IEEE Transactions on Applied Superconductivity, vol. 28, no. 4, 1-5.
4. Lanin, V. L., Ratnikau, E., Hatskevich, A. D. (2020). Improving the Efficiency of Induction Heating in the Air Gap of the Magnetic Circuit. Journal of Electronic Research and Application, vol. 4, no. 4, 1052. DOI: 10.26689/jera.v4i4.1052
5. Seehase, D., Kohlen, C., Neiser, A., Novikov, A., Nowottnick, M. (2018). Endogenous Heating of Printed Circuit Boards by Means of Electromagnetic Induction on Suitable Susceptors. 2018 41st International Spring Seminar on Electronics Technology (ISSE), p. 1-7, DOI: 10.1109/ISSE.2018.8443634
6. Hofmann, C., Frohlich, A., Kimme, J., Wiemer, M., Otto, T. (2019). A Novel Method for Mems Wafer-Level Packaging: Selective and Rapid Induction Heating for Copper-Tin Slid Bonding. 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), p. 277-280, DOI: 10.1109/TRANSDUCERS.2019.8808256
7. Drienovsky, M., Michalcova, E., Pekarcikova, M., Palcut, M., Frolek, L., Gogola, P., Gomory, F. (2018). Induction brazing of coated conductor high-temperature superconducting tapes with lead-free solder alloys. IEEE Transactions on Applied Superconductivity, vo. 28, no. 4, 1-5.
8. Wang, M., Huang, S. (2018). Study on Magnetic Pulse Assisted Brazing Process of 6063 Aluminum Alloy Pipe. Hot Working Technology, vol. 9, 21.
9. Lanin, V. L. (2018). Sizing up the efficiency of induction heating systems for soldering electronic modules. Surface Engineering and Applied Electrochemistry, vol. 54, no. 4, 401-406.
10. Seehase, D., Kohlen, C., Neiser, A., Novikov, A., & Nowottnick, M. (2018). Selective soldering on printed circuit boards with endogenous induction heat at appropriate susceptors. Periodica Polytechnica Electrical Engineering and Computer Science, vol. 62, no. 4, 172-180, DOI: 10.3311/PPee.13277
11. Yang, H., Wang, H., & Cao, D. (2016). Investigation of soldering for crystalline silicon solar cells. Soldering & Surface Mount Technology, vol. 28, no. 4, 222- 226, DOI: 10.1108/SSMT-04-2015-0015
12. Murygin, A. V., Tynchenko, V. S., Laptenok, V. D., Emilova, O. A., Bocharov, A. N. (2017). Complex of automated equipment and technologies for waveguides brazing using induction heating. IOP Conference Series: Materials Science and Engineering, vol. 173, no. 1, 012023, DOI: 10.1088/1757- 899X/173/1/012023
13. Tynchenko, V. S., Murygin, A. V., Petrenko, V. E., Seregin, Y. N., Emilova, O. A. (2017). A control algorithm for waveguide path induction brazing with product positioning. IOP Conference Series: Materials Science and Engineering, vol. 255, no. 1, 012018, DOI: 10.1088/1757-899X/255/1/012018
14. Milov, A. V., Tynchenko, V. S., Murygin, A. V. (2019). Experimental Verification of Flux Effect on Process of Aluminium Waveguide Paths Induction Brazing. International Russian Automation Conference, p. 282-291.
15. Milov, A. V., Tynchenko, V. S., Murygin, A. V. (2019). Neural Network Modeling to Control Process of Induction Brazing. 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), p. 1-5.
16. Li, S., Fairbank, M., Johnson, C., Wunsch, D. C., Alonso, E., Proao, J. L. (2013). Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions. IEEE transactions on neural networks and learning systems, vol. 25, no. 4, 738- 750, DOI: 10.1109/TNNLS.2013.2280906
17. Kalogirou, S. A. (1999). Applications of artificial neural networks in energy systems. Energy Conversion and Management, vol. 40, no. 10, 1073-1087, DOI: 10.1016/S0196-8904(99)00012-6.
18. Xia, C., Guo, P., Shi, T., Wang, M. (2004). Speed control of brushless DC motor using genetic algorithm based fuzzy controller. Proceeding of the 2004 International Conference on Intelligent Mechatronics and Automation, Chengdu, China, 3rd edn. A Treatise on Electricity and Magnetism, vol. 2, p. 68-73.
19. Juang, C. F., Hsu, C. H. (2009). Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control. IEEE Transactions on Industrial Electronics, vol. 56, no. 10, 3931-3940, DOI: 10.1109/ TIE.2009.2017557
20. Rusu, P., Petriu, E. M., Whalen, T. E., Cornell, A., Spoelder, H. J. (2003). Behavior-based neuro-fuzzy controller for mobile robot navigation. IEEE transactions on instrumentation and measurement, vol. 52, no. 4, 1335-1340, DOI: 10.1109/TIM.2003.816846
21. Bukhtoyarov, V. V., Milov, A. V., Tynchenko, V. S., Petrovsky, E. A., Tynchenko, S. V. (2019). Intelligently informed control over the process variables of oil and gas equipment maintenance. International Review of Automatic Control, vol. 12, no. 2, 59-66, DOI: 10.15866/ireaco.v12i2.16790.
22. Rutkovskaya, D., Pilinsky, M., Rutkovsky, L. (2006). Neural networks, genetic algorithms and fuzzy systems. Telekom, Moscow.
23. Murygin, A. V., Tynchenko, V. S., Laptenok, V. D., Emilova, O. A., Seregin, Y. N. (2017). Modeling of thermal processes in waveguide tracts induction brazing. IOP Conference Series: Materials science and engineering, vol. 173, no. 1, 012026, DOI: 10.1088/1757-899X/173/1/012026.