Intellectualizing the Process of Waveguide Tracks Induction Soldering for Spacecrafts

Vadim Sergeevich Tynchenko; Anton Vladimirovich Milov; Valeriya Valerievna Tynchenko; Vladimir Viktorovich Bukhtoyarov; Vladislav Viktorovich Kukartsev

doi:10.15866/irease.v12i6.16910

Intellectualizing the Process of Waveguide Tracks Induction Soldering for Spacecrafts

Vadim Sergeevich Tynchenko^(1*), Anton Vladimirovich Milov⁽²⁾, Valeriya Valerievna Tynchenko⁽³⁾, Vladimir Viktorovich Bukhtoyarov⁽⁴⁾, Vladislav Viktorovich Kukartsev⁽⁵⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irease.v12i6.16910

Abstract

The aim of this work is to develop an intelligent method for controlling the induction soldering process of spacecraft’s thin-walled aluminum waveguide tracks. This new control method is based on the use of intelligent data analysis in order to determine the optimal control algorithms for product heating and the workpiece motion control with optimal coefficients based on the operational data of the induction soldering technological process. The paper presents a mathematical formulation of the intelligent control problem. As part of this work, such intellectual methods have been chosen for comparison as a method based on a fuzzy logic, artificial neural networks, and a neuro-fuzzy controller. Based on experimental data on real technological processes of induction soldering, a comparative analysis of the effectiveness of the proposed approach has been carried out. The results of experimental studies have shown that the control method of the induction soldering technological process based on artificial neural networks has the highest efficiency. In addition, based on experimental data of real technological processes, the most effective structure of an artificial neural network has been determined. The highest recognition accuracy has been provided by an artificial neural network with five artificial neurons on each one of five hidden layers. Experimental quality control of the obtained artificial neural network has been carried out on three different waveguide tracks assemblies with different tube thicknesses: 58×25 mm, 35×15 mm, 19×9.5 mm. The results of the experimental verification have showed that for all the three waveguide tracks assemblies high-quality soldered joints have been obtained. The integrated application of intelligent technologies for controlling the technological process of induction soldering of thin-walled aluminum waveguide tracks could significantly improve the quality of permanently formed connections, as well as eliminate errors associated with human factor. Further research will focus on the intellectualization of other technological processes for creating permanent connections, such as electron beam welding, diffusion welding, etc. The results of this work clearly show the effectiveness of the proposed concept of intellectualization, so it is advisable to extend it to a wider range of technological processes.
Copyright © 2019 Praise Worthy Prize - All rights reserved.

Keywords

Waveguide Tracks; Spacecraft; Artificial Neural Networks; Fuzzy Logic; Neuro-Fuzzy Controller; Induction Soldering; Technical Process Control

Full Text:

PDF

References

S.K. Zlobin et al, Features of the production of waveguide-distributive tracts of antenna-feeder devices of space vehicles, Bulletin of the Siberian State Aerospace University 6 (2013), 145-157.

V. Rudnev, D. Loveless, R. Cook, Industrial applications of induction heating, Handb Induction Heating Routledge 3 (2017), 9-50.

P. Gierth, L. Rebenklau, A. Michaelis Evaluation of Soldering Processes for High Efficiency Solar Cells, 35th International Spring Seminar on Electronics Technology (ISSE), 133-137 (2012).
https://doi.org/10.1109/isse.2012.6273123

A. V. Murygin et al, Complex of Automated Equipment and Technologies for Waveguides Soldering using Induction Heating, IOP Conference Series: Materials Science and Engineering 173 (1), 012023 (2017).
https://doi.org/10.1088/1757-899x/173/1/012023

F. Nishimura et al, Development of a New Investment for High-frequency Induction Soldering, Dental Materials Journal 11 (1) (1992), 59-69,113.
https://doi.org/10.4012/dmj.11.59

H.Cai et al., Study on Multiple-Frequency IGBT High Frequency Power Supply for Induction Heating , Proceedings of the CSEE 2, 027 (2006).

V. L.Lanin, I. I.Sergachev, Induction Devices for Assembly Soldering in Electronics, Surface Engineering and Applied Electrochemistry 48 (4) (2012), 384-388.
https://doi.org/10.3103/s1068375512040114

V. Rudnev, D. Loveless, R. L. Cook, Handbook of induction heating (CRC press, Boca Raton, Florida, 2017).

V. S. Tynchenko et al, The Automated System for Technological Process of Spacecraft's Waveguide Paths Soldering, IOP Conference Series: Materials Science and Engineering 155, 012007 (2016).
https://doi.org/10.1088/1757-899x/155/1/012007

V. S. Tynchenko et al, A Control Algorithm for Waveguide Path Induction Soldering with Product Positioning1, IOP Conference Series: Materials Science and Engineering 255, 012018 (2017).
https://doi.org/10.1088/1757-899x/255/1/012018

Rusilawati, R., Soeprijanto, A., Wibowo, R., Reactualization of a Modified Single Machine to Infinite Bus Model to Multimachine System Steady State Stability Analysis Studies Using Losses Network Concepts and Radial Basis Function Neural Network (RBFNN), (2017) International Review on Modelling and Simulations (IREMOS), 10 (2), pp. 112-120.
https://doi.org/10.15866/iremos.v10i2.11207

Moghaddam, M., Mojallali, H., Neural Network Based Modeling and Predictive Position Control of Traveling Wave Ultrasonic Motor Using Chaotic Genetic Algorithm, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 370-379.

Ghazanfarpour, B., Radzi, M., Mariun, N., Adaptive Neural Network with Heuristic Learning Rule for Series Active Power Filter, (2013) International Review on Modelling and Simulations (IREMOS), 6 (6), pp. 1753-1759.

Mostefai, M., Miloudi, A., Miloud, Y., An Intelligent Maximum Power Point Tracker for Photovoltaic Systems Based on Neural Network, (2013) International Review on Modelling and Simulations (IREMOS), 6 (5), pp. 1477-1481.

Alexander, A., Thathan, M., Modelling and Simulation of Artificial Neural Network Based Harmonic Elimination Technique for Solar-Fed Cascaded Multilevel Inverter, (2013) International Review on Modelling and Simulations (IREMOS), 6 (4), pp. 1048-1055.

Pradeep, J., Devanathan, R., Fault Diagnosis of PMSM Using Artificial Neural Network, (2014) International Review on Modelling and Simulations (IREMOS), 7 (5), pp. 760-767.
https://doi.org/10.15866/iremos.v7i5.3156

A. V. Milov et al, Classification of Non-Normative Errors in Measurming Instruments Based on Data Mining, International Conference on Aviamechanical Engineering and Transport (AVENT 2018) 158, 432-437 (2018).
https://doi.org/10.2991/avent-18.2018.83

Peter, S., Kulkarni, S., Raglend, I., Simon, S., Wavelet Based Spike Propagation Neural Network (WSPNN) for Wind Power Forecasting, (2013) International Review on Modelling and Simulations (IREMOS), 6 (5), pp. 1513-1522.

Farahat, M., Elgawed, A., Mustafa, H., Ibrahim, A., Short Term Load Forecasting Using BP Neural Network Optimized by Particle Swarm Optimization, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 450-454.

Ananthamoorthy, N., Baskaran, K., Modelling, Simulation and Analysis of Fuzzy Logic Controllers for Permanent Magnet Synchronous Motor Drive, (2013) International Review on Modelling and Simulations (IREMOS), 6 (1), pp. 75-82.

Hanumanthakari, S., Kodad, S., Botlaguduru, S., Sensorless Direct Torque Control of Induction Motor Using AI Based Duty Ratio Controllers, (2016) International Review on Modelling and Simulations (IREMOS), 9 (5), pp. 339-347.
https://doi.org/10.15866/iremos.v9i5.10034

Soedibyo, A., Pamuji, F., Ashari, M., Grid Quality Hybrid Power System Control of Microhydro, Wind Turbine and Fuel Cell Using Fuzzy Logic, (2013) International Review on Modelling and Simulations (IREMOS), 6 (4), pp. 1271-1278.
https://doi.org/10.7813/2075-4124.2012/4-4/a.4

Bennassar, A., Abbou, A., Akherraz, M., A Comparative Study of IP, Fuzzy Logic and Sliding Mode Controllers in a Speed Vector Control of Induction Motor, (2013) International Review on Modelling and Simulations (IREMOS), 6 (6), pp. 1865-1871.

C. T. Lin, C. S. G. Lee, Neural-Network-Based Fuzzy Logic Control and Decision System, IEEE Transactions on Computers 40(12) (1989), 1320-1336.
https://doi.org/10.1109/12.106218

K. Premkumar, B. V. Manikandan, Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System, Applied Soft Computing 32 (2015), 403-419.
https://doi.org/10.1016/j.asoc.2015.04.014

Karthikumar, S., Mahendran, N., Sriraman, S., Implementation of Neuro-Fuzzy Controller to Reduce Output Voltage Ripple of KY Boost Converter, (2013) International Review on Modelling and Simulations (IREMOS), 6 (5), pp. 1410-1415.

D. Silver et al, Mastering the game of Go with deep neural networks and tree search, Nature 529(7587) (2016) 484.

G. Carleo, M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355 (6325) (2017), 602-606.
https://doi.org/10.1126/science.aag2302

W. Chine et al, A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks, Renewable Energy 90 (2016), 501-512.
https://doi.org/10.1016/j.renene.2016.01.036

S. Raith et al, Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data, Computers in biology and medicine 80 (2017), 65-76.
https://doi.org/10.1016/j.compbiomed.2016.11.013

P. E. Rauber et al, Visualizing the hidden activity of artificial neural networks, IEEE transactions on visualization and computer graphics 23(1) (2016), 101-110.
https://doi.org/10.1109/tvcg.2016.2598838

H. T. Nguyen, C. L. Walker, E. A. Walker, A first course in fuzzy logic (CRC press, Boca Raton, Florida, 2018).

J. L. Morgan et al, The fuzzy logic of network connectivity in mouse visual thalamus, Cell 165(1) (2016), 192-206.

C. W. De Silva, Intelligent control: fuzzy logic applications (CRC press, Boca Raton, Florida, 2018).

D. R. Parhi, P. K. Mohanty, IWO-based adaptive neuro-fuzzy controller for mobile robot navigation in cluttered environments, The International Journal of Advanced Manufacturing Technology 83(9-12) (2016), 1607-1625.
https://doi.org/10.1007/s00170-015-7512-5

J. Cervantes et al, Takagi–Sugeno dynamic neuro-fuzzy controller of uncertain nonlinear systems, IEEE Transactions on Fuzzy Systems 25(6) (2016), 1601-1615.
https://doi.org/10.1109/tfuzz.2016.2612697

L. C. Jain, A. Kandel, H. N. L. Teodorescu, Fuzzy and neuro-fuzzy systems in medicine (CRC press, Boca Raton, Florida, 2017).

F. Zahedi, Z. Zahedi, A review of neuro-fuzzy systems based on intelligent control, Journal of Electrical and Electronic Engineering 3(2-1) (2015), 58-61.
https://doi.org/10.11648/j.jeee.s.2015030201.23

R. Syahputra, Application of Neuro-Fuzzy Method for Prediction of Vehicle Fuel Consumption, Journal of Theoretical & Applied Information Technology 86(1) (2016), 138-150.

Bakouri, A., Mahmoudi, H., Abbou, A., Modeling and Robust Control with Wind Speed Estimation by Artificial Neural Networks of a DFIG Wind Turbine Under Both Normal Operation and Grid Fault, (2017) International Review of Electrical Engineering (IREE), 12 (2), pp. 100-109.
https://doi.org/10.15866/iree.v12i2.11343

Sawitri, D., Heryanto, M., Suprijono, H., Purnomo, M., Kusumoputro, B., Vibration-Signature-Based Inter-Turn Short Circuit Identification in a Three-Phase Induction Motor Using Multiple Hidden Layer Back Propagation Neural Networks, (2018) International Review of Electrical Engineering (IREE), 13 (2), pp. 98-106.
https://doi.org/10.15866/iree.v13i2.13881

Omar, H., A Geno-Fuzzy Fast Attitude Controller for Satellites Stabilized by Reaction WheelsA Geno-Fuzzy Fast Attitude Controller for Satellites Stabilized by Reaction Wheels, (2018) International Journal on Engineering Applications (IREA), 6 (5), pp. 150-155.
https://doi.org/10.15866/irea.v6i5.16628

Monadjemi, S., Moallem, P., Automatic Diagnosis of Particular Diseases Using a Fuzzy-Neural Approach, (2018) International Journal on Engineering Applications (IREA), 6 (1), pp. 29-34.
https://doi.org/10.15866/irea.v6i1.15143
v6i1.15143

Mohanty, K., Fuzzy Control of Wind Cage Induction Generator System, (2017) International Journal on Energy Conversion (IRECON), 5 (4), pp. 122-129.
https://doi.org/10.15866/irecon.v5i4.13755

Refbacks

There are currently no refbacks.

Username
Password
Remember me