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Bera, Tapas
- On Dissimilar Welding of AISI 304 and EN 8 Steels through Metal Active Gas Welding : Part I-Parametric Optimization Using Taguchi’s Orthogonal Array
Abstract Views :186 |
PDF Views:3
Authors
Tapas Bera
1,
Santanu Das
2
Affiliations
1 Department of Metallurgical and Materials Engineering Indian Institute Technology Kharagpur, Kharagpur - 700032, IN
2 Department of Mechanical Engineering Kalyani Government Engineering College, Kalyani - 741235, IN
1 Department of Metallurgical and Materials Engineering Indian Institute Technology Kharagpur, Kharagpur - 700032, IN
2 Department of Mechanical Engineering Kalyani Government Engineering College, Kalyani - 741235, IN
Source
Indian Welding Journal, Vol 55, No 3 (2022), Pagination: 60-70Abstract
Gas metal arc welding is a flexible technique for joining numerous metallic materials, both similar and dissimilar. AISI 304 stainless steel and EN 8 medium carbon steel plates are welded in this experiment. 100% CO2 gas is used as a shielding gas in this method. Experiments are planned using the Taguchi technique, which employs a three-column, nine-row orthogonal array. This design is chosen based on three welding parameters, each of which has three levels. Heat input, root gap, and torch angle are being used as welding parameters for this investigation. Grey relational analysis approach is utilized for optimization purposes. S/N ratio is calculated for each level of process parameters. Because this experiment aims at maximizing the Grey relational grade (GRG), the best configuration for input parameters is the one with the most significant S/N ratio. Analysis of variance is employed to analyze the significance of input parameters. It is found that sample 9 has the highest GRG of 0.861431. So, the sound weld joint can be obtained at the optimum level where the values of input parameters have heat input of 0.747 kJ/mm, root gap of 2 mm and torch angle of 45°. It is quite challenging to weld dissimilar materials. In this work, a sound weld joint is achieved in between AISI 304 stainless steel and EN 8 medium carbon steel flats, and optimum results are effectively determined.Keywords
GMAW, MAG Welding, Dissimilar Welding, GRA, Taguchi Analysis, ANOVA.
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- 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
Abstract Views :173 |
PDF Views:3
Authors
Tapas Bera
1,
Santanu Das
2
Affiliations
1 Department of Metallurgical and Materials Engineering Indian Institute Technology Kharagpur, Kharagpur, 700032, IN
2 Department of Mechanical Engineering Kalyani Government Engineering College, Kalyani-741235, IN
1 Department of Metallurgical and Materials Engineering Indian Institute Technology Kharagpur, Kharagpur, 700032, IN
2 Department of Mechanical Engineering Kalyani Government Engineering College, Kalyani-741235, IN
Source
Indian Welding Journal, Vol 55, No 3 (2022), Pagination: 71-78Abstract
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.Keywords
ANN, Regression Equation, GMAW, ANOVA, MATLAB, MINITAB.References
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