On Dissimilar Welding of AISI 304 and EN 8 Steels through Metal Active Gas Welding : Part II-Estimation of Weld Characteristics Using Regression Analysis and Neural Networks
Authors
-
Department of Metallurgical and Materials Engineering Indian Institute Technology Kharagpur, Kharagpur, 700032 ,IN
Tapas Bera -
Department of Mechanical Engineering Kalyani Government Engineering College, Kalyani-741235 ,INSantanu Das
DOI:
https://doi.org/10.22486/iwj.v55i3.213078Keywords:
ANN, Regression Equation, GMAW, ANOVA, MATLAB, MINITAB.Abstract
Nowadays, researchers have been using several predicting tools in the areas of defense, marketing, finance, and engineering. In the area of welding processes, estimation of response parameters is done. As a predicting tool in this investigation, artificial neural networks (ANN) and regression equations are used. Using the ANN model, predictions can be made through various learning methods possible with this algorithm. The regression equation for each response parameter is obtained from MINITAB software. Weld bead geometry, hardness, and maximum bending load of the welded zone are predicted. Sets of input and output data needed for experimental runs are obtained by joining AISI 304 and EN 8 steels together using the GMAW process. To predict weld bead geometry and mechanical properties of the weld zone of dissimilar steels, two separate prediction tools are used. The outcomes are then compared. Such research is novel in the field of predicting and comparing the output parameters of different weld joints using ANN and regression analysis (RA). It is concluded that ANN as well as regression equations have predicted the weld bead geometry, hardness, and maximum bending load with a little error. It is also found that ANN provides satisfactory predicted results with much less error than the results obtained from the regression equation.
References
Kumar R, Kundu S and Kumar P (2015); Parameters Optimization for Gas Metal Arc Welding of Austenitic Stainless Steel (AISI 304) and Low Carbon Steel using Taguchi's Technique, International Journal of Engineering and Management Research, 5(5), pp.342-347.
Singh S and Gupta N (2016); Analysis of Hardness in Metal Inert Gas Welding of Two Dissimilar Metals, Mild Steel & Stainless Steel, International Organization of Scientific Research Journal of Mechanical and Civil Engineering, 13(3), pp.94-113.
Chaudhari PD and More NN (2014); Effect of Welding Process Parameters on Tensile Strength, IOSR Journal of Engineering, 4(5), pp.01-05.
Patel CN and Chaudhary S (2013); Parametric Optimization of Weld Strength of Metal Inert Gas Welding and Tungsten Inert Gas Welding by Using Analysis of Variance and Grey Relational Analysis, International Journal of Research in Modern Engineering and Emerging Technology, 1(3), pp.48-56.
Sabiruddin K, Bhattacharya S and Das S (2013); Selection of appropriate process parameters for gas metal arc welding of medium carbon steel specimens, International Journal of the Analytic Hierarchy Process, 5(2), pp.252-267.
Correia DS, Goncalves CV, da Cunha SS and Ferraresi VA (2005); Comparison between genetic algorithms and response surface methodology in GMAW optimization, Journal of Materials Processing Technology, 160, pp.70-76.
Singh V, Chandrasekaran M and Thiruganana-sambandam M (2019); Artificial Neural Network Modelling of Weld Bead Characteristics during GMAW of Nitrogen Strengthened Austenitic Stainless Steel, AIP Conference Proceedings 2128, 020024 (2019).
Bera T and Das S (2021); Application of Artificial Neural Networks in Predicting Output Parameters of Gas Metal Arc Welding of Dissimilar Steels, Indian Science Cruiser, 35(3), pp.26-30.
Bera T and Das S (2021); Estimation of Geometry and Properties of Weld Bead Using Artificial Neural Networks, Reason- A Technical Journal, 20, pp.46-56.
Chan B, Pacey J and Bibby M (1999); Modelling gas metal arc weld geometry using artificial neural network technology, Journal of Canadian Metallurgical Quarterly, 38(1), pp.43-51.
Sarkar A and Das S (2016); Selection of appropriate process parameters for gas metal arc welding of a Steel under 100% carbon dioxide gas shield, Indian Welding Journal, 49(4), pp.61-70.
Saha MK, Das S, Bandyopadhyay A and Bandyopadhyay S (2012); Application of L6 orthogonal array for optimal selection of some process parameters in GMAW process, Indian Welding Journal, 45(4), pp.41-50.
Nagesh DS and Datta GL (2008); Modeling of fillet welded joint of GMAW process: integrated approach using DOE, ANN and GA, International Journal on Interactive Design Manufacturing, 2, pp.127-136.
Shah J, Patel G and Makwana J (2017); Optimization and Prediction of MIG Welding Process Parameters Using ANN, International Journal of Engineering Development and Research, 5, pp.1487-1491.
Ramos-Jaime D and Lopez-Juarez I (2010); ANN and linear regression model comparison for the prediction of bead geometrical properties in automated welding, 1st International Congress on Instrumentation and Applied Science, pp.1-10.
Addamani R, Ravindra HV and Gayathri devi SK (2020); Estimation and Comparison of Welding Performances for ASTM A 106 Material in P-GMAW Using GMDH and ANN, Journal of Critical Reviews, 7(14), pp.2606-2613.
Sreeraj P, Kannan T and Maji S (2013); Simulation and Parameter Optimization of GMAW Process Using Neural Networks and Particle Swarm Optimization Algorithm, International Journal of Mechanical Engineering and Robotic Research, 2(1), pp.131-146.
Sreeraj P and Kannan T (2015); Modelling and Prediction of Stainless Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models, Advances in Mechanical Engineering, 4, pp.1-12.
Gunaraj V and Murugan N (1999); Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes, Journal of Materials Processing Technology, 88, pp.266-275.
Lee J and Um K (2000); A comparison in a back-bead prediction of gas metal arc welding using multiple regression analysis and artificial neural network, Journal of Optics and Lasers in Engineering, 34, pp.149-158.
Bera T, Santra S and Das S (2022); Performance Measure of Resistance Spot Welding of Similar and Dissimilar Triple Thin Sheets by Using AHP-ANN Hybrid Network, Indian Science Cruiser, 36(2), pp.35-41.
