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

G. Senthilkumar; R.  Ramakrishnan

doi:10.6180/jase.202106_24(3).0011

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

G. Senthilkumar This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and R. Ramakrishnan²

¹Research Scholar, Department of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India
²Professor & Head, Department of Sports Technology, Tamilnadu Physical Education and Sports University, Chennai, India

Received: July 18, 2020
Accepted: December 17, 2020
Publication Date: June 1, 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.202106_24(3).0011

ABSTRACT

The aim of this work is to recommend a method to model a friction welding process parameters for ASTM A516 Grade 70 alloy steel using techniques like regression analysis, fuzzy logic and finite element analysis. The techniques used in this study were used to determine the welding process variable by which the expected burn off length is obtained in friction welding. The 9 set of input parameters like friction pressure, upset pressure, forging time and rotational speed and corresponding output parameter burn off length gathered based on L9 Orthogonal array. While error estimated for regression analysis is 6.01 %, finite element analysis is 4.98 % marginally outperforms, fuzzy logic analysis which is yielded error only 3.26 %. The fuzzy logic analysis is the proposed methodology used to predict burn off length parameter for any set of welding input parameters. This model helps us to find out the actual length of the material in the joining process.

Keywords: Burn off length, Regression Analysis, Fuzzy Logic Analysis, Finite Element Analysis.

REFERENCES

[1] Dawei Zhao, Yuanxun Wang, Dongjie Liang, and Mikhail Ivanov. Performances of regression model and artificial neural network in monitoring welding quality based on power signal. Journal of Materials Research and Technology, 9(2):1231–1240, 2020.
[2] Bo Ram Lee, Tae Jong Yun, Won Bin Oh, Gang Zhang, Kyoung Seo Ki, and Ill Soo Kim. Global and Clusterwise Regression Models for Total Bead Area as Welding Quality. In Procedia Manufacturing, volume 30, pages 26–33, 2019.
[3] M Islam, A Buijk, M Rais-Rohani, and K Motoyama. Simulation-based numerical optimization of arc welding process for reduced distortion in welded structures. Finite Elements in Analysis and Design, 84:54–64, 2014.
[4] Gang Zheng, Sayeed Hossain, Ed Kingston, Christopher E. Truman, and David J. Smith. An optimisation study of the modified deep-hole drilling technique using finite element analyses applied to a stainless steel ring welded circular disc. International Journal of Solids and Structures, 118-119:1339–1351, 2017.
[5] Athanasios Kolios, Lin Wang, Ali Mehmanparast, and Feargal Brennan. Determination of stress concentration factors in offshore wind welded structures through a hybrid experimental and numerical approach. Ocean Engineering, 178:38–47, 2019.
[6] Bappa Acherjee, Arunanshu S Kuar, Souren Mitra, and Dipten Misra. Modeling of laser transmission contour welding process using FEA and DoE. Optics and Laser Technology, 44(5):1281–1289, 2012.
[7] Yi Luo, Chuntian Li, and Huibin Xu. Modeling of resistance spot welding process using nonlinear regression analysis and neural network approach on galvanized steel sheet. In Advanced Materials Research, volume 291- 294, pages 823–828, 2011.
[8] B. Sung, I. Kim, Y. Xue, H. Kim, and Y. Cha. Fuzzy Regression Model to Predict the Bead Geometry in the Robotic Welding Process. Acta Metallurgica Sinica (English Letters), 20(6):391–397, 2007.
[9] Mohammad Mahdi Tafarroj and Farhad Kolahan. A comparative study on the performance of artificial neural networks and regression models in modeling the heat source model parameters in GTA welding. Fusion Engineering and Design, 131:111–118, 2018.
[10] Dawei Zhao, Yuanxun Wang, Dongjie Liang, and Peng Zhang. Modeling and process analysis of resistance spot welded DP600 joints based on regression analysis. Materials and Design, 110:676–684, 2016.
[11] Kyu Jung Yeom, Woo Sik Kim, and Kyu Hwan Oh. Integrity assessment of API X70 pipe with corroded girth and seam welds via numerical simulation and burst test experiments. Engineering Failure Analysis, 70:375–386, 2016.
[12] Juliana P.B. Souza, Ricardo A.A. Aguiar, Hector R.M. Costa, João M.L. Reis, and Pedro M.C.L. Pacheco. Numerical modelling of the mechanical behavior of hybrid joint obtained by spot welding and bonding. Composite Structures, 202:216–221, 2018.
[13] Mantra Prasad Satpathy, Bikash Ranjan Moharana, Shailesh Dewangan, and Susanta Kumar Sahoo. Modeling and optimization of ultrasonic metal welding on dissimilar sheets using fuzzy based genetic algorithm approach. Engineering Science and Technology, an International Journal, 18(4):634–647, 2015.
[14] A Krishnamoorthy, S. Rajendra Boopathy, K. Palanikumar, and J. Paulo Davim. Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics. Measurement: Journal of the International Measurement Confederation, 45(5):1286–1296, 2012.
[15] Rupesh Chalisgaonkar and Jatinder Kumar. Parametric optimization and modelling of rough cut WEDM operation of pure titanium using grey-fuzzy logic and dimensional analysis. Cogent Engineering, 1(1), 2014.
[16] G. Senthil Kumar and R. Ramakrishnan. 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, 2020.
[17] Fung Huei Yeh, Ching Lun Li, and Kun Nan Tsay. Application of adaptive network fuzzy inference system to die shape optimal design in sheet metal bending process. Journal of Applied Science and Engineering, 15(1):31–40, 2012.
[18] Long Jiang, Yuanfang Cheng, Yiheng Zhang, Chuanliang Yan, and Zhongying Han. Numerical investigation on the freezing evolution process of rock mass at sub-zero temperature. Journal of Applied Science and Engineering, 22(2):337–348, 2019.
[19] Yanxin Yang, Qinke Wang, Jianlin Ma, and Fengjuan Huang. Parametric analysis of slope stability improved with soil-cement using numerical method. Journal of Applied Science and Engineering, 22(3):449–458, 2019.
[20] Ajitanshu Vedrtnam, Gyanendra Singh, and Ankit Kumar. Optimizing submerged arc welding using response surface methodology, regression analysis, and genetic algorithm. Defence Technology, 14(3):204–212, 2018.
[21] M Mehta, G. M. Reddy, A. V. Rao, and A. De. Numerical modeling of friction stir welding using the tools with polygonal pins. Defence Technology, 11(3):229–236, 2015.
[22] Shu Huang, Rui Chen, Hang Zhang, Jiamei Ye, Xiaole Yang, and Jie Sheng. A study of welding process in connecting borosilicate glass by picosecond laser pulses based on response surface methodology. Optics and Laser Technology, 131, 2020.
[23] Mumin Sahin. Joining of stainless-steel and aluminium materials by friction welding. International Journal of Advanced Manufacturing Technology, 41(5-6):487– 497, 2009.
[24] P Sathiya, S Aravindan, and A Noorul Haq. Friction welding of austenitic stainless steel and optimization of weld quality. International Symposium of Research Students on Materials Science and Engineering, pages 1– 10, 2004.
[25] Aditya Khamparia, Babita Pandey, Devendra Kr Pandey, Deepak Gupta, Ashish Khanna, and Victor Hugo C. de Albuquerque. Comparison of RSM, ANN and Fuzzy Logic for extraction of Oleonolic Acid from Ocimum sanctum. Computers in Industry, 117, 2020.