Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

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1.60

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G. Senthilkumar This email address is being protected from spambots. You need JavaScript enabled to view it.1, T. Mayavan2, and R. Ramakrishnan3

1Department of Mechanical Engineering, Panimalar Institute of Technology, Chennai, Tamilnadu, India
2Department of Mechanical Engineering, Panimalar Engineering College, Chennai, Tamilnadu, India
3Department of Sports Technology, Tamilnadu Physical Education and Sports University, Chennai, India


 

Received: May 11, 2021
Accepted: November 9, 2021
Publication Date: December 22, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202210_25(5).0008  


ABSTRACT


The Intention of this work is to state a design of process parameters in the continuous drive friction welding of ASTM A516 Grade 70 steel, 12 mm diameter and 100 mm length circular rods by using central composite design (CCD) in response surface methodology (RSM). The ASTM A516 Grade 70 steel finds its extensive usage in pump shafts and heat exchangers. In this work friction pressure/ time (MPa/s), upset pressure/ time (MPa/s) and rotational speed (rps) are fed into the central composite design as input parameters. The input and output relationship are modeled to estimate axial shortening, impact toughness and ultimate tensile strength of welded joints. The optimization was accomplished to maximize impact toughness (J) & ultimate tensile strength (MPa) and minimize axial shortening (mm). The confirmation test was also conducted by setting the optimized parameters. The microstructure of the weldment and heat affected zone (HAZ) of welded specimens has been examined and shown in this study. At optimized conditions the ultimate tensile strength, impact toughness and axial shortening obatained are 512.37MPa, 18.86J and 15.82 mm respectively. The error between optimum conditions and experimental run for the properties ultimate tensile strength, impact toughness and axial shortening predicted are 0.67%,3.37% and 9.06% respectively. This work narrates a method to get better welding conditions over a wide search in lesser number of trials.


Keywords: Response Surface Methodology, Central Composite Design, Ultimate Tensile Strength, Impact Toughness, Axial Shortening


REFERENCES


  1. [1] S. Selvamani and K. Palanikumar, (2014) “Optimizing the friction welding parameters to attain maximum tensile strength in AISI 1035 grade carbon steel rods" Measurement 53: 10–21. DOI: 10.1016/j.measurement.2014.03.008.
  2. [2] S. Selvamani, K. Palanikumar, K. Umanath, and D. Jayaperumal, (2015) “Analysis of friction welding parameters on the mechanical metallurgical and chemical properties of AISI 1035 steel joints" Materials & Design (1980-2015) 65: 652–661. DOI: 10.1016/j.matdes.2014.09.056.
  3. [3] P. Ajith, B. K. Barik, P. Sathiya, and S. Aravindan, (2015) “Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel" Defence Technology 11(2): 157–165. DOI: 10.1016/j.dt.2015.03.001.
  4. [4] P. Sathiya, S. Aravindan, A. N. Haq, and K. Paneerselvam, (2009) “Optimization of friction welding parameters using evolutionary computational techniques" Journal of materials processing technology 209(5): 2576–2584. DOI: 10.1016/j.jmatprotec.2008.06.030.
  5. [5] M. J. Varkey, A. Sumesh, and K. R. Kumar, (2020) “A computational approach in optimizing process parameters influencing the heat input and depth of penetration of tungsten inert gas welding of austenitic stainless steel (AISI 316L) using response surface methodology" Materials Today: Proceedings 24: 1199–1209. DOI: 10.1016/j.matpr.2020.04.434.
  6. [6] S. J. S. Chelladurai, K. Murugan, A. P. Ray, M. Upadhyaya, V. Narasimharaj, and S. Gnanasekaran, (2021) “Optimization of process parameters using response surface methodology: A review" Materials Today: Proceedings 37: 1301–1304. DOI: 10.1016/j.matpr.2020.06.466.
  7. [7] U. Patil and M. Kadam, (2020) “Multiobjective optimization of MMAW process parameters for joining stainless steel 304 with mild steel by using response surface methodology" Materials Today: Proceedings 26: 305–310. DOI: 10.1016/j.matpr.2019.11.277.
  8. [8] G. Liu, X. Gao, C. Peng, Y. Huang, H. Fang, Y. Zhang, D. You, and Z. Nanfeng, (2020) “Optimization of laser welding of DP780 to Al5052 joints for weld width and lapshear force using response surface methodology" Optics & Laser Technology 126: 106072. DOI: 10.1016/j.optlastec.2020.106072.
  9. [9] S. Huang, R. Chen, H. Zhang, J. Ye, X. Yang, and J. Sheng, (2020) “A study of welding process in connecting borosilicate glass by picosecond laser pulses based on response surface methodology" Optics & Laser Technology 131: 106427. DOI: 10.1016/j.optlastec.2020.106427.
  10. [10] A. Hafeez, S. A. A. Taqvi, T. Fazal, F. Javed, Z. Khan, U. S. Amjad, A. Bokhari, N. Shehzad, N. Rashid, S. Rehman, et al., (2020) “Optimization on cleaner intensification of ozone production using Artificial Neural Network and Response Surface Methodology: Parametric and comparative study" Journal of Cleaner Production 252: 119833. DOI: 10.1016/j.jclepro.2019.119833.
  11. [11] S. A. A. Daniel, R. Pugazhenthi, R. Kumar, and S. Vijayananth, (2019) “Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and Taguchi-grey relational analysis" Defence Technology 15(4): 545–556. DOI: 10.1016/j.dt.2019.01.001.
  12. [12] G. Magudeeswaran, S. R. Nair, L. Sundar, and N. Harikannan, (2014) “Optimization of process parameters of the activated tungsten inert gas welding for aspect ratio of UNS S32205 duplex stainless steel welds" Defence technology 10(3): 251–260. DOI: 10.1016/j.dt.2014.06.006.
  13. [13] G. Rambabu, D. B. Naik, C. V. Rao, K. S. Rao, and G. M. Reddy, (2015) “Optimization of friction stir welding parameters for improved corrosion resistance of AA2219 aluminum alloy joints" Defence Technology 11(4): 330–337. DOI: 10.1016/j.dt.2015.05.003.
  14. [14] B. Shanmugarajan, R. Shrivastava, P. Sathiya, and G. Buvanashekaran, (2016) “Optimisation of laser welding parameters for welding of P92 material using Taguchi based grey relational analysis" Defence Technology 12(4): 343–350. DOI: 10.1016/j.dt.2016.04.001.
  15. [15] G. Senthilkumar and R. Ramakrishnan, (2021) “Design of Optimal Parameter for Solid-State Welding of EN 10028-P355 GH Steel Using gray Incidence Reinforced Response Surface Methodology" Arabian Journal for Science and Engineering 46(3): 2613–2628. DOI:10.1007/s13369-020-05169-z.
  16. [16] G. Senthilkumar, R. Ramakrishnan, et al., (2021) “A Comparative Study of Predicting Burn off Length in Continuous Drive Solid State Friction Welding for ASTM A516 Steel by Regression Analysis, Fuzzy Logic Analysis and Finite Element Analysis" Journal of Applied Science and Engineering 24(3): 359–366. DOI: 10.6180/jase.202106_24(3).0011.
  17. [17] G. S. Kumar and R. Ramakrishnan, (2020) “Influence of mechanical characteristics of friction welded ferrite stainless steel joint through novel mathematical model using buckingham’s pi theorem" International Journal of Mechanical and Production Engineering Research and Development 10(1): 185–198. DOI: 10.24247/ijmperdfeb202016.
  18. [18] P. Sathiya, S. Aravindan, and A. N. Haq, (2007) “Effect of friction welding parameters on mechanical and metallurgical properties of ferritic stainless steel" The International Journal of Advanced Manufacturing Technology 31(11-12): 1076–1082. DOI: 10.1007/s00170-005-0285-5.


    



 

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