Hybrid Taguchi Based Grey Algorithm for Multi Objective Optimization of Gas Metal Arc Welded DP1000 Steel

Abstract

Euro-6 norms for reduction of CO2 emissions and ECE-R66-01 regulation for safer passenger transportation became legislative obligations for bus builders. These two adaptations increase the weight of a bus as nearly 600kg which also increases the fuel consumption nearly 1.5-3%. Dual phase (DP) steels can compensate these increases by decreasing the thickness of the components. Because, ferrite provides the steel with good formability and martensite increases the hardness and ultimate tensile strength in DP steels. This study focused on the multi response hybrid optimization of gas metal arc welding (GMAW) of DP1000 steel to determine the optimal parametric combination which gives the maximum ultimate tensile strength (UTS, MPa) and joint efficiency (JE) under minimum heat input (Q, kj/mm). Nine experimental trials which is based on the orthogonal array suggested by Taguchi method was carried out to investigate objective functions for optimization in the experimental range of GMAW parameters such as voltage (U, volt), welding speed (V, mm/min) and wire feed rate (f, m/min). The Taguchi method continued with Grey Relational Analysis (GRA) to overcome the multi-objective optimization model. Finally, Analysis of variance (ANOVA) method has been used to determine the significance of the welding parameters on the responses of UTS, JE and Q. This shows flexible applicability of Taguchi based GRA to find out the effect of each GMAW process parameters onto individual responses.

Keywords

• DP1000 steel
• Taguchi method
• Gas Metal arc welding
• Grey relation optimization

References

1. [1] Karatas, M., (2018) Optimization of GMAW Process Parameters for Improving Mechanical Properties of DP1000 Steel Joint, MSc Thesis, Cukurova University Institute of Natural and Applied Sciences.
2. [2] http://www.lincolnelectric.com, (2014) Gas Metal Arc Welding. The Lincoln Electric Company, 22801 Saint Clair Avenue, Cleveland, OH, 44117, U.S.A.
3. [3] Martinez R.T., Bestard, G.A., Silva, A.M.A., Alfaro, S.C.A., (2021), Analysis of GMAW process with deep learning and machine learning techniques, Journal of Manufacturing Processes 62, 695–703.
4. [4] Huang, Y., Yang, D., Wang, K., Wang, L., Fan, J., (2020), A quality diagnosis method of GMAW based on improved empirical mode decomposition and extreme learning machine, Journal of Manufacturing Processes 54 ,120-128.
5. [5] Sivasakthivel, P.S., Sudhakaran, R., (2020), Modelling and optimisation of welding parameters for multiple objectives in pre-heated gas metal arc welding process using nature instigated algorithms, Australian Journal of Mechanical Engineering, 18, 76-87.
6. [6] Chauhan, M.J., (2017), Optimization of parameters for gas metal arc welding of mild steel using Taguchi’s technique, International Journal of Current Engineering and Scientific Research, 4(8), 1-8.
7. [7] Aghakhani, M., Mehrdad, E., Hayati, E., (2011), Parametric Optimization of Gas Metal Arc Welding Process by Taguchi Method on Weld Dilution, International Journal of Modeling and Optimization, 1(3) , 216-220.
8. [8] Kumar, A., Khurana, M.K., Pradeep, K. Y., (2016), Optimization of Gas Metal Arc Welding Process Parameters, IOP Conf. Series: Materials Science and Engineering, 149, 1-11.

9. [9] Rizvi, S.A., Wajahat, A., (2020), Multi attribute decision making parametric optimization in weld bead by gas metal arc welding through grey relation analysis: A case study, International Journal of Engineering, Science and Technology, 12(2), 59-66.
10. [10] Arya, DM., Chaturvedi V., Vimal, J., (2013), Parametric optimization of mig process parameters using Taguchi and grey Taguchi analysis, International journal of research in engineering & applied sciences, 3(6), 2249-3905.
11. [11] Esme, U., Bayramoglu, M., Kazancoglu, Y., Ozgun, S., (2009), Optimization of Weld Bead Geometry in Tig Welding Process Using Grey Relation Analysis And Taguchi Method, Materiali in Tehnologije, 43(3), 143-149.
12. [12] Srivastava, S., Garg, R.K., (2017), Process parameter optimization of gas metal arc welding on IS: 2062 mild steel using response surface methodology, Journal of Manufacturing Processes 25, 296–305.
13. [13] Singla M., Singh, D., Deepak, D., (2010), Parametric Optimization of Gas Metal Arc Welding Processes by Using Factorial Design Approach, Journal of Minerals & Materials Characterization & Engineering, 9(4), 353-363.
14. [14] Ambekar, S.D., Wadhokar, S.R., (2015), Parametric Optimization of Gas metal arc welding process by using Taguchi method on stainless steel AISI 410, Modern Engineering and Emerging Technology, 3(1), 1-9.
15. [15] Yadav, R.K., Jain, N., Thakur, K., (2015), Parametric Optimisation of Gas Metal arc Welding Process with the Help of Taguchi Method on Tensile Strength – A Review, International Journal of Engineering Research & Technology, 4(4), 223-226.
16. [16] Sreeraj, P., Kannan, T., Maji, S., (2013), Optimization of GMAW Process Parameters Using Particle Swarm Optimization, ISRN Metallurgy, 2013 1-10.
17. [17] Shao, Q., Xu, T., Yoshino, T., Song, N., (2017), Multi-objective optimization of gas metal arc welding parameters and sequences for low-carbon steel (Q345D) T-joint, Journal of Iron and Steel Research, Journal of Iron and Steel Research, International 21, 511- 555.
18. [18] Kshirsagar, R., Jones, S., Lawrence, J., Tabor, J., (2020), Optimization of TIG Welding Parameters Using a Hybrid Nelder Mead-Evolutionary Algorithms Method, Journal of Manufacturing and Materials Processing, 4(10), 1-21.
19. [19] Datta, S., Bandyopadhyay, A., Kumar, P.P., (2008), Grey-Based Taguchi Method for Optimization of Bead Geometry in Submerged Arc Bead-On-Plate Welding, International Journal of Advanced Manufacturing Technology volume 39, 1136-1143.
20. [20] http://www.ssab.com.tr/products/brands/docol/products/docol-1000dp, (2009), Welding of Domex and Docol Advanced High Strength Steels. SSAB Tunnplat AB SE-781 84 Borlange, Sweden [accessed 30 June 2018].