Parametric Studies on TIG Welding of 316L Stainless Steel by RSM and TLBO

Moi, Shubhash Chandra; Rudrapati, Ramesh; Bandyopadhyay, Asish; Pal, P. K.

doi:10.4028/www.scientific.net/msf.969.744

Cited by 7 publications

(3 citation statements)

References 19 publications

(20 reference statements)

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“…In this work, the mathematical correlation between input parameters and their responses for precise prediction of optimal values of DOP and BW were developed using RSM. A regression equation (second-order) to represent the response surface is given by 38 Where, ‘Z’ is the output response, A 1 , B 1 , and C 1 are input process parameters, and δ represents the coefficients of all input parameters.…”

Section: Resultsmentioning

confidence: 99%

Parameter optimization and experimental validation of A-TIG welding of super austenitic stainless steel AISI 904L using response surface methodology

Sunny

Korra

Vasudevan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Super austenitic stainless steel (SASS) is a premium stainless steel (SS) variant which finds applications in challenging environments. One of the premium grades of SS that falls under the said category is AISI 904L. Autogenous activated flux tungsten inert gas (A-TIG) welding is an established technique for fabricating sound steel joints which offers various advantages. In this context, this research aims to identify an optimum set of input process parameters to effectively employ A-TIG welding to fabricate 10 mm thick joints of SASS AISI 904L plates by employing bead on plate welding trials. The input parameters varied for the trials are welding current, torch travel speed, and percentage of Argon gas present in a mixture of Argon and Helium – which is the shielding gas used, while the responses were the depth of weld penetration (DOP) and weld bead width (BW). For finalizing the combinations of input parameters and the run order of the welding trials, the design of experiments (DOE) was performed using central composite design (CCD) of response surface methodology (RSM). After performing the trials, the DOP and BW attained were measured and the data thus generated were analyzed to study the influence of varying input parameters on the two responses. Analysis of variance (ANOVA) was then performed to check the input parameter's level of significance. RSM's desirability approach was used for numerical optimization to attain maximum DOP and a moderate BW. A linear regression equation generated can be used to precisely predict DOP and BW for any random values of input parameters. The bead on plate trial's validation study displayed root mean squared error (RMSE) values of 0.328 and 0.653 for DOP and BW respectively, The RMSE values of DOP and BW of the joint fabricated with optimized parameters was 0 and 0.311 respectively.

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Section: Resultsmentioning

confidence: 99%

Parameter optimization and experimental validation of A-TIG welding of super austenitic stainless steel AISI 904L using response surface methodology

Sunny

Korra

Vasudevan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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show abstract

“…It was observed during the microstructural investigation process, the interdendritic space of the weld rises with greater heat input. Moi et al [19] analysed the effect of welding process parameters such as welding current, speed, and gas flow rate on 316 SS plates by the conventional welding process. They used RSM as a mathematical model for input and output parameters.…”

Section: Introductionmentioning

confidence: 99%

Welding investigation of Hastelloy C-276 and parametric optimization using integrated MRA-TLBO algorithm during A-TIG process

Singh,

Singh

et al. 2024

Eng. Res. Express

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A-TIG welding is a modified form of GTAW process that has become popular due to its ability to create high-quality welded joints with minimal cracks and distortion. This technique is commonly used to join various metals, including stainless steel, aluminum, titanium, and nickel alloys like Hastelloy C-276, which is used in fabrication of complex components across marine, nuclear, aerospace, and chemical industries. In this investigation, A-TIG welding of Hastelloy C-276 was studied using three parameters welding current, welding speed, and gas flow rate at three distinct levels. The impact of these parameters were analyzed on two responses, namely penetration depth and width of weld. Macrostructural investigation, sensitivity analysis, and parametric studies revealed that welding current (I) had the most influence, followed by speed of welding (S) and flow rate of gas (G). Full quadratic multiple regression analysis-based models were developed for both responses, which were found to be suitable with a higher coefficient of determination and an average percentage error of 2.165% and 2.624%, respectively. Additionally, a teaching learning based optimization algorithm was integrated with these models to identify the most effective A-TIG parameter combination for higher penetration depth and lower weld width. This MRA-TLBO integrated approach resulted in an optimal parametric combination of I (170A), S (180mm/min), and G (11.37 l/min) that resulted in a penetration depth and weld width of 4.503 mm and 4.684 mm, respectively. Validation at this optimal setting showed an improvement of 3.365% and 1.284% for weld width and penetration depth, respectively, suggesting the robustness of the developed methodology.

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“…The welding input parameters shape the weld joint's quality, making welding a sophisticated process with multiple inputs and outputs [1]. The weld bead's geometry is paramount because it governs the weld's mechanical characteristics.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Weld Qualities in Various Welding Operations Using Advanced Computational Techniques: A Comprehensive Review

2024

IJSREM

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This study explores various computational methodologies for optimizing the weld-bead geometry and parameters in different welding processes. It encompasses a range of techniques, including linear progression, response surface methodology, artificial neural networks, Taguchi techniques, and genetic algorithms. The optimization of process parameters significantly impacts productivity, quality, and production costs. This review highlights the profound effects of these optimization methodologies on manufacturing processes and demonstrates their integral role in welding. Detailed insights into each method are provided, forming a robust foundation for further research and development in welding procedure optimization. Keywords: Weld Bead Geometry, Optimization Techniques, Computational Methods, Welding Processes, Neural Networks

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Parametric Studies on TIG Welding of 316L Stainless Steel by RSM and TLBO

Cited by 7 publications

References 19 publications

Parameter optimization and experimental validation of A-TIG welding of super austenitic stainless steel AISI 904L using response surface methodology

Parameter optimization and experimental validation of A-TIG welding of super austenitic stainless steel AISI 904L using response surface methodology

Welding investigation of Hastelloy C-276 and parametric optimization using integrated MRA-TLBO algorithm during A-TIG process

Optimization of Weld Qualities in Various Welding Operations Using Advanced Computational Techniques: A Comprehensive Review

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