Simultaneously Modeling and Optimization of Heat Affected Zone and Tensile Strength in GTAW Process Using Simulated Annealing Algorithm

Subject Areas : Manufacturing & Production

Meysam Beytolamani ¹ , Masoud Azadi Moghaddam ² , Farhad Kolahan ³

1 - Graduate Student, Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
2 - PhD. Candidate, Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
3 - Associate Professor, Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Received: 2020-10-02 Accepted : 2021-03-06 Published : 2021-05-01

Keywords: Optimization, Simulated annealing (SA) algorithm, Thin sheet, gas tungsten arc welding (GTAW) process,

Abstract :

In the present study, a technique has been addressed in order to model and optimize gas tungsten arc welding (GTAW) process which is one of the mostly used welding processes based on the high quality fabrication acquired. The effects of GTAW process variables on the joint quality of AISI304 stainless steel thin sheets (0.5 mm) have been investigated. The required data for modeling and optimization purposes has been gathered using Taguchi design of experiments (DOE) technique. Next, based on the acquired data, the modeling procedure has been performed using regression functions for two outputs; namely, heat affected zone (HAZ) width and ultimate tensile stress (UTS). Then, analysis of variance (ANOVA) has been performed in order to select the most fitted proposed models for single-objective and multi-criteria optimization of the process in such a way that UTS is maximized and HAZ width minimized using simulated annealing (SA) algorithm. Frequency, welding speed, base current and welding current are the most influential variables affecting the UTS at 22%, 21%, 20% and 17% respectively. Similarly, base current, welding current, frequency and welding speed affect the HAZ at 28%, 20%, 16%, and 15% respectively. Based on the results considering the lowest values for current results in the smallest amount of HAZ. By the same token in order to acquire the largest amount of UTSs the highest values of current must be considered. Setting welding and base current, frequency, speed, and debi at 42 and 5 apms, 46 Hz, 0.4495 m/min, and 5 lit/min respectively resulted the optimized HAZ and UTS simultaneously. The proper performance of the proposed optimization method has been proved through comparison between computational results and experimental data with less than 6% error.

References:

 [1] H.T. Lee, S.L. Jeng, “Characteristics of dissimilar welding of alloy 690 to 304L stainless steel”, Sci Technol Weld Joining, Vol. 6, 2001, pp. 225–34.

[2] H. Jamshidi Aval, A. Farzadi, S. Serajzadeh, A.H. Kokabi, “Theoretical and experimental study of microstructures and weld pool geometry during GTAWof 304 stainless steel”, Int J Adv Manuf Technol, Vol. 5, 2008, pp.1663-1676.

[3]. J. Yan, M. Gao, X. Zeng, “Study on microstructure and mechanical properties of 304 stainless steel joints by TIG, laser and laser-TIG hybrid welding”, Optics and Lasers in Engineering, Vol. 48, 2010, pp.512–517.

[4]. A. K. Srirangan, S. Paulraj, “Multi-response optimization of process parameters for TIG welding of Incoloy 800HT by Taguchi grey relational analysis”, Engineering Science and Technology, an International Journal, Vol. 4, 2015, pp. 54-67.

[5] F. Kolahan, M. Heidari, “A new approach for predicting and optimizing weld bead geometry in GMAW”, Int. J. Aerosp. Mech. Eng. Vol. 5, 2011, pp.138–142.

[6] M. Azadi Moghaddam, R. Golmazerji, F. Kolahan, “Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN-PSO approach”, Measurement, Vol. 6, 2016, pp. 123-136.

[7] S.V. Sapakal, M.T. Telsang, “Parametric optimization of MIG welding using Taguchi design method”, Int. J. Adv. Eng. Res. Study, Vol. 1, 2012, pp. 28–37.

[8] M. Balasubramanian, “Prediction of optimum weld pool geometry of PCTIG welded titanium alloy using statistical design”, Eng. Sci. Technol. Int. J. Vol. 5, 2015, pp. 106-120.

[9] R. B. Jose, R. Wagner, M. N. David, A. J. Alves, V. J. Dias, “Pulsed Nd:YAG laser welding of AISI 304 to AISI 420 stainless steels”. Opt Laser Eng, Vol. 12, 2007, pp. 45-60.

[10] E.M. Anawa, A.G. Olabi, “Optimization of tensile strength of ferritic/austenitic laser-welded components”, Opt Laser Eng, Vol. 3, 2008, pp. 571–577.

[11]. D.S. Nagesh, G.L. Datta, “Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process”, Applied Soft Computing, Vol. 10, 2010, pp. 897–907.

[12] "Guidelines for Gas Tungsten Arc Welding (GTAW)", Miller Electric Mfg. Co., from 'www.MillerWelds.com', 2008.

[13] F. Kolahan, R. Golmezerji, , M. Azadi Moghaddam, “Multi Objective Optimization of Turning Process using Grey Relational Analysis and Simulated Annealing Algorithm”, Applied Mechanics and Materials, Vol. 4, 2012, pp. 2926-2932.

[14] M. Azadi Moghaddam, R. Golmazerji, F. Kolahan, “Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN-PSO approach”, Measurement, Vol. 12, 2016,pp. 132- 143.

[15] F. Kolahan, M. Azadi Moghaddam, “The use of Taguchi method with grey relational analysis to optimize the EDM process parameters with multiple quality characteristics”, Scientia Iranica B, Vol. 22, 2015, pp. 530-538

[16]. B. Senthilkumar,  T. Kannan, “Effect of flux cored arc welding process parameters on bead geometry in super duplex stainless steel claddings”, Measurement. Vol. 62, 2015, pp. 127-136.

 [17] S.S. Chong, A.R.A. Aziz, S.W. Harun,  H. Arof, S. Shamshirband, “Application of Multiple Linear Regression, Central Composite Design, and ANFIS models in Dye concentration Measurement and Prediction Using Plastic Optical Fiber Sensor”, Measurement,  Vol. 10,  2015, pp. 16-29. 

[18] D. Jaimon,  S. Quadros, A. Khan, A. J. Antony, “Modelling of Suddenly Expanded Flow Process in Supersonic Mach Regime using Design of Experiments and Response Surface Methodology”, JCAMECH, Vol. 49, 2018, pp. 149-160.

[19] S. Ghorbanhosseini, F. Fereshteh-saniee, “Multi-objective optimization of geometrical parameters for constrained groove pressing of aluminium sheet using a neural network and the genetic algorithm”, JCAMECH, Vol. 50, 2019, pp. 275-281

 [20] M. Vishwakarma, V,V.K. Parashar, “Regression Analysis and Optimization of Material Removal Rate on Electric Discharge Machine for EN-19 Alloy Steel”, International Journal of Scientific and Research Publications, Vol. 2, 2012, pp. 167-175.

[21] D. Vishnu, R.I. Asal, T. Patel, B. Alok, “Optimization of Process Parameters of EDM Using ANOVA Method”, International Journal of Engineering Research and Applications, Vol. 3, 2013, pp. 1119-1125.

[22] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, “Optimization by simulated annealing. American Association for the Advancement of Science”, Vol. 220, 1983, pp. 671–680.

[23] M.H.M.A. Jahromi, R. Tavakkoli-Moghaddam, A. Makui, A. Shamsi, “Solving an one-dimensional cutting stock problem by simulated annealing and tabu search”, Journal of Industrial Engineering International, Vol. 8, 2012, pp. 1-8.