Research on the possibilities of using linear observation models in welding processes

Ameer Abdulkadhim Oudah Shamkhee, Shepelev Anatoliy Fedorovich, Finaev Valeriy Ivanovich, Zargaryan Elena Valerevna

Abstract


The usage relevance of linear observation models of welding processes is substantiated. A brief analytical review of scientific papers on using linear observation models for researching the processes of arc welding is introduced. An example of studies utilizing a linear observation model based on the results of experiments with alloying of a weld metal is given. The type of linear observation model – fractional replication – is determined. An experiment planning matrix and test results are given. After performing tests and processing the experimental data, mathematical models in the form of dependencies of the transfer of alloying elements into the weld metal were obtained. According to the results of the experiments, empirical models in the form of dependencies evaluating the percentages of manganese, silicon and carbon, transfer coefficients from the “wire-to-product” distance, the diameter of the welding nozzle and the filling wire feed rate were obtained. Since the degree of uncertainty of parameters during welding is high enough, the necessity of further modification of linear observation models using expert knowledge is justified. The mathematical specification of the linear observation model with parameters in the form of fuzzy intervals is given. An algorithm for identifying the parameters of a linear observation model in the form of fuzzy intervals has been developed.

Keywords


Metal welding observation , model regression ,shielding gas, plasma-arc welding weld, bead alloying prediction ,control

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References


Wu CS, Wang L, Ren WJ, Zhang XY. Plasma arc welding: Process, sensing, control and modeling. Journal of manufacturing processes. 2014 Jan 1;16(1):74-85.

Moore KL, Abdelrahman MA, Naidu DS. Gas metal arc welding control-II. Control strategy. Nonlinear Analysis: Theory, Methods & Applications. 1999 Jan 1;35(1):85-93.

Zhou K, Cai L. A nonlinear current control method for resistance spot welding. IEEE/ASME Transactions on Mechatronics. 2013 Mar 19;19(2):559-69.

Kim IS, Basu A, Siores E. Mathematical models for control of weld bead penetration in the GMAW process. The International Journal of Advanced Manufacturing Technology. 1996 Nov 1;12(6):393-401..

Khanna P, Maheshwari S. Development of mathematical models for prediction and control of weld bead dimensions in MIG welding of stainless steel 409M. Materials Today: Proceedings. 2018 Jan 1;5(2):4475-88.

Son JS, Lee JP, Park MH, Jin BJ, Yun TJ, Kim IS. A study on on-line mathematical model to control of bead width for arc welding process. Procedia engineering. 2017 Jan 1;174:68-73.

Xue Y, Kim IS, Son JS, Park CE, Kim HH, Sung BS, Kim IJ, Kim HJ, Kang BY. Fuzzy regression method for prediction and control the bead width in the robotic arc-welding process. Journal of Materials Processing Technology. 2005 May 15;164:1134-9.

Besekersky VA, Popov EP. Theory of systems of automatic control. M: Nauka. 1975.

Subbanna SR. Analysis of Proportional Integral and Optimized Proportional Integral Controllers for Resistance Spot Welding System (RSWS)–A Performance Perspective. InIOP Conference Series: Materials Science and Engineering 2017 Aug (Vol. 225, No. 1, p. 012175). IOP Publishing.

Chu WH, Tung PC. Development of an automatic arc welding system using a sliding mode control. International Journal of Machine Tools and Manufacture. 2005 Jun 1;45(7-8):933-9.

Huang YW, Tung PC, Wu CY. Tuning PID control of an automatic arc welding system using a SMAW process. The International Journal of Advanced Manufacturing Technology. 2007 Aug 1;34(1-2):56-61.

Fonseca CM, Fleming PJ. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary computation. 1995 Mar;3(1):1-6.

Adekunle AA, Ogbeide SO, Olorunfemi BJ, Adekunle OR. Development of computer aided design software for expert systems in welding. Journal of Emerging Trends in Engineering and Applied Sciences. 2016 Jun 1;7(3):95-102.

Chen C, Lv N, Chen S. Data-Driven Welding Expert System Structure Based on Internet of Things. InTransactions on Intelligent Welding Manufacturing 2018 (pp. 45-60). Springer, Singapore.

Almasani SA, Finaev VI, Qaid WA, Tychinsky AV. The Decision-making Model for the Stock Market under Uncertainty. International Journal of Electrical & Computer Engineering (2088-8708). 2017 Oct 1;7(5).

Antonini JM. Health effects of welding. Critical reviews in toxicology. 2003 Jan 1;33(1):61-103.

Deyev GF. Surface phenomena in fusion welding processes. cRc Press; 2005 Dec 19.

Dubois D, Prade H, editors. Fundamentals of fuzzy sets. Springer Science & Business Media; 2012 Dec 6.

Elangovan M, Thenarasu M. Design of flexible spot welding cell for Body-In-White (BIW) assembly. Periodicals of Engineering and Natural Sciences. 2018 Oct 19;6(2):23-38.

Durakovic B. Design for Additive Manufacturing: Benefits, Trends and Challenges. Periodicals of Engineering and Natural Sciences. 2018 Dec 11;6(2):179-91.

Mizumoto M, Tanaka K. Fuzzy sets and their operations. Information and Control. 1981 Jan 1;48(1):30-48.

Ulubeyli S, Kazaz A, Arslan V. A structured selection process for small and medium enterprises in construction industry: case of international projects. Periodicals of engineering and natural sciences. 2017 Oct 18;5(3)..

Nzioka AM. Kinetic Study of the Thermal Decomposition for Mixed Municipal Solid Waste Using Thermogravimetric Analysis. Periodicals of Engineering and Natural Sciences. 2017 Oct 18;5(3)..

Kucuk SD, Gerengi H, Guner Y. The Effect of Tinuvin Derivatives as an Ultraviolet (UV) Stabilizer on EPDM Rubber. Periodicals of Engineering and Natural Sciences. 2018 Mar 16;6(1):52-62.

Musa KM, Shattnan AT, Saleh AH. Manufacturing Enamel Resin Using Furancarboxalehyde-3 Compound. Journal of Computational and Theoretical Nanoscience. 2019 Jan 1;16(1):130-3.

S. Rawan and A. Manal, “Real time data analysis and visualization for the breast cancer disease,” Period. Eng. Nat. Sci., vol. 7, no. 1, pp. 395–407, 2019.

D. Y. Mahmood, A. G. Ismaeel, and A. H. Taqi, “Mining Method for Cancer and Pre-Cancer Detection Caused by Mutant Codon 248 in TP53,” Period. Eng. Nat. Sci., vol. 7, no. 2, pp. 522–533, 2019.

Z. YILMAZ, M. OKSAR, and F. BASCIFTCI, “Multi-Objective Artificial Bee Colony Algorithm to Estimate Transformer Equivalent Circuit Parameters,” Period. Eng. Nat. Sci., vol. 5, no. 3, pp. 271–277, 2017.

T. TUNCER, “SCSO: A novel sine-cosine based swarm optimization algorithm for numerical function optimization,” Period. Eng. Nat. Sci., vol. 6, no. 2, pp. 1–9, 2018.




DOI: http://dx.doi.org/10.21533/pen.v7i3.657

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Copyright (c) 2019 Ameer Abdulkadhim Oudah Shamkhee, Shepelev Anatoliy Fedorovich, Finaev Valeriy Ivanovich, Zargaryan Elena Valerevna

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License