Journal of Applied Science and Engineering

Published by Tamkang University Press


Impact Factor



G. Senthilkumar 1, T.Mayavan2, R.Murugan1, and G.Gnanakumar1

1Department of Mechanical Engineering, Panimalar Institute of Technology, Chennai, Tamilnadu, India.
2Department of Mechanical Engineering, Panimalar Engineering College, Chennai, Tamilnadu, India.


Received: September 16, 2022
Accepted: October 11, 2022
Publication Date: November 24, 2022

 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: ||  


Ever since the Bronze Age, permanent fastening of materials has always been considered a good technique in the mechanical engineering field and it has now attained a gradual enhancement to get defect free joint. The continuous drive solid state rotary friction welding machine can make quality joint and emissions is almost nil. This study explores the possibility of using a solid-state welding process on EN 10028-P355 GH steel and AISI 430 Steel. In this research work, selected materials of 16 mm diameter rods are joined with help of friction welding to bring down Axial Shortening and improve Tensile Strength, Impact toughness of the joint. The selected materials find extensive applications in pump shafts, boilers, and pressure vessels. The frictional pressure, upset pressure, frictional time, upset time, and rotational speed are the input factors with three levels each that have been considered for this work. The experiment involves an L27 Orthogonal Array. The merits of a grey theory are combined with the statistical analyzing capabilities of response surface methodology in an integrated approach of grey incidence reinforced response surface methodology to select the optimal friction welding inputs. The optimal friction welding inputs were validated with proper experiments. The improvement of the properties attained for the dissimilar EN 10028 P355GH Steel & AISI 430 Steel joint is 1.93%, 5%, and 11.5% of maximum ultimate tensile strength, impact toughness, and axial shortening respectively. The study will offer the guiding database for welding steel in a solid state using continuous drive friction welding.

Keywords: Friction Welding, Tensile Strength, Impact Toughness, Axial Shortening, Optimization, Response Surface Methodology


  1. [1] J. Akram, P. R. Kalvala, V. Jindal, and M. Misra, (2018) “Evaluating location specific strain rates, temperatures, and accumulated strains in friction welds through microstructure modeling" Defence Technology 14(2): 83–92. DOI: 10.1016/j.dt.2017.11.002.
  2. [2] R. Kumar, R. Singh, I. Ahuja, A. Amendola, and R. Penna, (2018) “Friction welding for the manufacturing of PA6 and ABS structures reinforced with Fe particles" Composites Part B: Engineering 132: 244–257. DOI: 10.1016/j.compositesb.2017.08.018.
  3. [3] N. R. J. Hynes, M. V. Prabhu, and P. Nagaraj, (2017) “Joining of hybrid AA6063-6SiCp-3Grp composite and AISI 1030 steel by friction welding" Defence technology 13(5): 338–345. DOI: 10.1016/j.dt.2017.05.014.
  4. [4] K. Aoki and T. Koezawa, (2017) “Characteristics of friction welding within a short time for aluminum alloy deformed by ECAE process" Procedia engineering 207: 597–602. DOI: 10.1016/j.proeng.2017.10.1027.
  5. [5] T. H. Tra and M. Sakaguchi, (2016) “High cycle fatigue behavior of the IN718/M247 hybrid element fabricated by friction welding at elevated temperatures" Journal of Science: Advanced Materials and Devices 1(4): 501–506. DOI: 10.1016/j.jsamd.2016.08.009.
  6. [6] R. Palanivel, R. Laubscher, I. Dinaharan, and D. Hattingh, (2017) “Microstructure and mechanical characterization of continuous drive friction welded grade 2 seamless titanium tubes at different rotational speeds" International journal of pressure vessels and piping 154: 17–28. DOI: 10.1016/j.ijpvp.2017.06.005.
  7. [7] M. Kimura, K. Suzuki, M. Kusaka, and K. Kaizu, (2017) “Effect of friction welding condition on joining phenomena, tensile strength, and bend ductility of friction welded joint between pure aluminium and AISI 304 stainless steel" Journal of Manufacturing Processes 25: 116–125. DOI: 10.1016/j.jmapro.2016.12.001.
  8. [8] M. Kimura, K. Suzuki, M. Kusaka, and K. Kaizu, (2017) “Effect of friction welding condition on joining phenomena and mechanical properties of friction welded joint between 6063 aluminium alloy and AISI 304 stainless steel" Journal of Manufacturing Processes 26: 178–187. DOI: 10.1016/j.jmapro.2017.02.008.
  9. [9] P. Li, J. Li, and H. Dong, (2017) “Analytical description of heat generation and temperature field during the initial stage of rotary friction welding" Journal of Manufacturing Processes 25: 181–184. DOI: 10.1016/j.jmapro.2016.12.003.
  10. [10] D. R. Pissanti, A. Scheid, L. F. Kanan, G. Dalpiaz, and C. E. F. Kwietniewski, (2019) “Pipeline girth friction welding of the UNS S32205 duplex stainless steel" Materials & Design 162: 198–209. DOI: 10.1016/j.matdes.2018.11.046.
  11. [11] S. Porchilamban and J. R. Amaladas, (2019) “Structural relationships of metallurgical and mechanical properties influenced by Ni-based fillers on Gas Tungsten Arc Welded Ferritic/Austenitic SS dissimilar joints" Journal of Advanced Mechanical Design, Systems, and Manufacturing 13(1): JAMDSM0023–JAMDSM0023. DOI: 10.1299/jamdsm.2019jamdsm0023.
  12. [12] F. Jin, J. Li, P. Liu, X. Nan, X. Li, J. Xiong, and F. Zhang, (2019) “Friction coefficient model and joint formation in rotary friction welding" Journal of Manufacturing Processes 46: 286–297. DOI: 10.1016/j.jmapro.2019.09.008.
  13. [13] M. H. Shirvani, (2020) “A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems" Engineering Applications of Artificial Intelligence 90: 103501. DOI: 10.1016/j.engappai.2020.103501.
  14. [14] R. N. Talouki, M. H. Shirvani, and H. Motameni, (2021) “A hybrid meta-heuristic scheduler algorithm for optimization of workflow scheduling in cloud heterogeneous computing environment" Journal of Engineering, Design and Technology: DOI: 10.1108/JEDT-11-2020-0474.
  15. [15] 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.
  16. [16] 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.
  17. [17] G. Senthilkumar, T. Mayavan, R. Ramakrishnan, et al., (2021) “Optimization for Friction Welding Input Factors to Maximize Tensile Strength and Minimize Axial Shortening in ASTM A516 Grade 70 steel Rods" Journal of Applied Science and Engineering 25(5): 773–784. DOI: 10.6180/jase.202210_25(5).0008.
  18. [18] 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.
  19. [19] S. Raja and A. J. Rajan, (2022) “A decision-making model for selection of the suitable FDM machine using fuzzy TOPSIS" Mathematical Problems in Engineering 2022: DOI: 10.1155/2022/7653292.
  20. [20] Y. Ramzanpoor, M. Hosseini Shirvani, and M. Golsorkhtabaramiri, (2022) “Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure" Complex & Intelligent Systems 8(1): 361–392. DOI: 10.1007/s40747-021-00368-z.
  21. [21] M. Hosseini Shirvani and R. Noorian Talouki, (2022) “Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach" Complex & Intelligent Systems 8(2): 1085–1114. DOI: 10.1007/s40747-021-00528-1.