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Bera, Tapas
- The History of Development of Gas Metal Arc Welding Process
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Authors
Affiliations
1 Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani – 741235, West Bengal, IN
1 Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani – 741235, West Bengal, IN
Source
Indian Science Cruiser, Vol 34, No 6 (2020), Pagination: 64-66Abstract
Gas metal arc welding (GMAW) process can be divided as a metal inert gas (MIG) welding and metal active gas (MAG) welding process. In the process, the electric arc is produced which is used to melt and fuse the given materials. Inert or active shielding gases are passed through the nozzle to protect the weld pool from atmospheric contamination. The development of MIG welding technique has been started in the 19th century when Humphry Davy acquired the electric arc in 1800. From the implementation of inert gas at that time to the use of carbon dioxide gas (CO2), the gas metal arc welding process went through a remarkable development, and that is why it is widely used nowadays in automobile, railway construction, ship buildings, power plant industry, etc. In this paper, the chronological developments of the gas metal arc welding process are discussed.Keywords
Welding, Arc Welding, GMAW, MAG, MIG, Gas Shielding, Robotic Welding.
References
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- Application of Artificial Neural Networks in Predicting Output Parameters of Gas Metal Arc Welding of Dissimilar Steels
Abstract Views :574 |
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Authors
Tapas Bera
1,
Santanu Das
1
Affiliations
1 Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani-741235, IN
1 Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani-741235, IN
Source
Indian Science Cruiser, Vol 35, No 3 (2021), Pagination: 26-30Abstract
Artificial Neural Network (ANN) can be used for prediction utilizing some learning method. Gas metal arc welding (GMAW) was reported in a previous work to join SS304L stainless steel and EN8 mild steel plates. The experimental data obtained are used for training the ANN to enable it predict the output. ANN model is constructed to estimate ultimate tensile strength, elongation and hardness of the weld joint. A data set is tested through the modeled ANN to have satisfactory results. Quite close estimation of the ANN predicted values can be made with the observed ultimate tensile strength, elongation and hardness of the weld joint.Keywords
GMAW, ANN, Dissimilar Welding, MIG, Modeling, MATLAB.References
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- P Kah, J Suoranta and J Martikainen, Advanced Gas Metal Arc Welding Process, The International Journal of Advanced Manufacturing Technology, Springer Nature Switzerland AG, Vol 67, page 655-674, 2012.
- O P Khanna, A Text of Welding Technology, Dhanpat Rai Publications, New Delhi, 2001.
- S Pal, S K Pal and A K Samantaray, Artificial Neural Network Modeling of Weld Joint Strength Prediction of a Pulsed Metal Inert Gas Welding Process Using Arc Signals, Journal of Materials Processing Technology, Vol 202, page 464-474, 2008.
- D S Nagesh and G L Datta, Modeling of Fillet Welded Joint of GMAW Process: Integrated Approach Using DOE, ANN and GA, International Journal on Interactive Design Manufacturing, Vol 2, page 127-136, 2008.
- P Sreeraj, T Kannan and S Maji, Simulation and Parameter Optimization of GMAW Process Using Neural Networks and Particle Swarm Optimization Algorithm, International Journal of Mechanical Engineering and Robotic Research, Vol 2, page 131-146, 2013.
- R Addamani, H V Ravindra and S K Gayathri devi, Estimation and Comparison of Welding Performances for ASTM A 106 Material in P-GMAW Using GMDH and ANN, Journal of Critical Reviews, Vol 7, page 2606-2613, 2020.
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- P Sreeraj and T Kannan, Modelling and Prediction of Stainless-Steel Clad Bead Geometry Deposited by GMAW Using Regression and Artificial Neural Network Models, Advances in Mechanical Engineering, Vol 4, page 1-12, 2015.
- B N Sreeharan, T Kannan and P Aravind, Process Optimization of GMAW over AA6351 Aluminium Alloy Using ANN, Vol 8, page 208-218, 2017.
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- Estimation of Geometry and Properties of Weld Bead Using Artificial Neural Networks
Abstract Views :323 |
PDF Views:152
Authors
Affiliations
1 Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani-741235, IN
1 Department of Mechanical Engineering, Kalyani Government Engineering College, Kalyani-741235, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 20 (2021), Pagination: 46-56Abstract
Investigations on prediction or estimation of output responses in welding using artificial neural networks (ANN) have become popular among researchers. In this work, metal active gas (MAG) welding was implemented to join EN-8D medium carbon steel plates together by varying welding current, weld voltage and torch traverse speed as input parameters. Depth of penetration, reinforcement, hardness and bend angle at failure were the responses. Then the input parameters and output parameters are used to train the neural networks in this work. Feed forward network with Levenberg-Marquardt training function is implemented. 3-10-4 model of ANN is used for the prediction of depth of penetration, reinforcement, hardness and bend angle. From the regression chart, it is found that the designed model predicted the results of both replications with quite less error and hence, the effectiveness of the technique.Keywords
GMAW; ANN; Neural network; MAG welding; Modeling; MATI_ABFull Text
References
- Khanna, O. R, A Text Book of Welding Technology, Dhanpat Rai Publications, New Delhi, 2001.
- Nadkarni, S.V., Modern Arc Welding Technology, Oxford & IBM Publishing Co. Pvt. Ltd., New Delhi.
- Bera, T, The History of Development of Gas Metal Arc Welding process, Indian Science Cruiser, Vol 34, No 4, pp. 64-66, 2020.
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- Bera, T. and Das, S., Application of Artificial Neural Networks in Predicting Output Parameters of Gas Metal Arc Welding of Dissimilar Steels, Indian Science Cruiser, Vol. 35, No. 3, pp. 26-30,2021.
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- Das, S., Roy R. and Chattopadhyay, A.B., Evaluation of Wear of Turning Carbide Inserts Using Neural Networks, International Journal of Machine Tools and Manufacture, Vol.36, No.7, pp.789-797, 1996.
- Das, S., Bandyopadhyay, P.P. and Chattopadhyay, A.B., Neural-Networks- Based Tool Wear Monitoring in Turning Medium Carbon Steel Using a Coated Carbide Tool, Journal of Materials Processing Technology, Vol.63, No.1-3, pp. 187-192,1997.
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- Performance Measure of Resistance Spot Welding of Similar and Dissimilar Triple Thin Sheets by using AHP-ANN Hybrid Network
Abstract Views :160 |
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Authors
Affiliations
1 Department of Metallurgical and Materials Engineering, IIT Kharagpur - 721302,, IN
2 Department of Production Engineering, Jadavpur University, Kolkata - 700032,
3 Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, West Bengal, IN
1 Department of Metallurgical and Materials Engineering, IIT Kharagpur - 721302,, IN
2 Department of Production Engineering, Jadavpur University, Kolkata - 700032,
3 Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, West Bengal, IN
Source
Indian Science Cruiser, Vol 36, No 2 (2022), Pagination: 33-39Abstract
The analytical hierarchy process or AHP is a useful decision-making tool, and it is applied in this work in resistance spot welding where two different types of triple thin sheets consisting of aluminium, galvanized iron and stainless steel are joined. Combining both the AHP and ANN, a hybrid network is developed to eliminate the complexity of the experimental results to predict. The AHP-ANN hybrid network successfully predicted output parameters with less error. Correlation coefficient has been more than 0.98 and the applicability of this method..Keywords
ANN, AHP, Resistance Spot Welding, Welding, Dissimilar Welding, Hybrid NetworkReferences
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- A U Khan, and Y Ali, Analytical Hierarchy Process (AHP) and Analytic Network Process Methods and Their Applications: A Twenty Year Review from 2000–2019, International Journal of the Analytic Hierarchy Process, Vol 12, No 3, 2020.
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- P Sreeraj, T Kannan, S Maji, Simulation and Parameter Optimization of GMAW Process Using Neural Networks and Particle Swarm Optimization Algorithm, International Journal of Mechanical Engineering and Robotic Research, Vol 2, No 1, page. 131-146, 2013.
- J Lee, K Um, 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, Vol 34, page. 149-158, 2000.
- H Ates, Prediction of gas metal arc welding parameters based on artificial neural networks, Materials and Design, Vol 28, page. 2015-2023, 2007.
- T Bera, S Das, Application of Artificial Neural Networks in Predicting Output Parameters of Gas Metal Arc Welding of Dissimilar Steels, Indian Science Cruiser, Vol 35, No 3, page. 26-30, 2021.
- DSNagesh, GL Datta, Modeling of fillet welded joint of GMAW process: integrated approach using DOE,
- ANN and GA, International Journal on Interactive Design Manufacturing, Vol 2, page. 127-136, 2008.
- BN Sreeharan, T Kannan, P Aravind, Process Optimization of GMAW Over AA6351 Aluminium Alloy Using ANN, Vol 8, No 9, page. 208-218, 2017.
- T Bera, S Das, Estimation of Geometry and Properties of Weld Bead Using Artificial Neural Networks, Reason- A Technical Journal, Vol 20, page. 46-56, 2021.
- 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 :183 |
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.References
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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
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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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