Optimizing submerged arc welding using response surface methodology, regression analysis, and genetic algorithm

Vedrtnam, Ajitanshu; Singh, Gyanendra Kumar; Kumar, Ankit

doi:10.1016/j.dt.2018.01.008

Cited by 52 publications

(16 citation statements)

References 9 publications

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“…It should be emphasized that there is a factually justified possibility of the complimentary application of various analyses enabling the more precise investigation of issues. Examples of the above-named approach include the application of various cluster analysis techniques [27][28][29][30], cluster analysis and regression analysis [1], principal component analysis and the design of experiment (Taguchi method) [33], principal component analysis and neural network analysis [36] and other variants [36,37].…”

Section: Discussionmentioning

confidence: 99%

Application of Multivariate Analysis Methods in Welding Engineering

Fydrych¹,

Świerczyńska²,

Rogalski³

et al. 2018

eBIS

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Phenomena and processes taking place during welding are usually very complex and, for this reason, should be described using multivariate methods. The article discusses the methodological basis and selected application areas as regards the solving of welding problems using statistical multivariate methods. In addition, the article presents exemplary applications of the design of experiment, multiple regression analysis, cluster analysis, principal component analysis and logistic regression analysis. The application of multivariate analyses provides the possibility of performing the mathematical description of joining processes, which, after verification, could be used to predict results of such processes, particularly in relation to the properties and fatigue service life of welded structures.

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Section: Discussionmentioning

confidence: 99%

Application of Multivariate Analysis Methods in Welding Engineering

Fydrych¹,

Świerczyńska²,

Rogalski³

et al. 2018

eBIS

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show abstract

“…The Taguchi desirability approach has been used for discussion and analyzation of the data and also a confirmatory test has been conducted. Vedrtnam and Singh [24] selected welding current, arc voltage, welding speed, and nozzle to plate distance as weld input parameters and performed submerged arc welding on stainless steel. Response surface methodology and genetic algorithm have been used for experimentation and validation of the data, and it has been found that the predicted values are similar to the experimented values.…”

Section: Introductionmentioning

confidence: 99%

Experimental studies of Regulated Metal Deposition (RMD™) on ASTM A387 (11) steel: study of parametric influence and welding performance optimization

Dinbandhu

Prajapati

Vora

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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The present study aims to highlight the effect of the governing variables in regulated metal deposition (RMD™) on the mechanical properties and weld quality of low alloy steel. Current, voltage, and gas flow rate have been considered as main (RMD™) parameters, whereas heat-affected zone, depth of penetration, bead width, and bead height have been taken into account as welding performance characteristics. Signal to noise has been utilized in order to determine the optimal welding parameters, while analysis concerning the significant level of determination has been assessed using analysis of variance. Furthermore, the exploration of principal component analysis and combined quality loss concept integrated with the Taguchi method has been introduced to assess the favorable welding condition. The optimal favorable machine combination has been obtained as current = 100 A, V = 13 V, and gas flow rate = 21 L/min using both the methods.

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“…The reported use of RSM in electromagnetic research is less whereas in the areas such as chemical engineering and manufacturing engineering, RSM has become an established practice to formulate empirical relationships between the required set of design parameters and response variables [14][15][16][17]. The present paper is an attempt to employ the concept of RSM to study the parametric relationship between the design parameters of DEMPA and Return Loss (RL) at resonant frequency.…”

Section: Introductionmentioning

confidence: 99%

Predicting Performance Characteristics of Double Elliptical Micro-Strip Patch Antenna for Radiolocation Applications Using Response Surface Methodology

Jose¹,

Rekh²,

Jose³

2020

PIER B

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Double Elliptical Micro-strip Patch Antenna (DEMPA) is a newer family of patch antennas which possesses higher design flexibility and has greater potential for getting miniaturized than Elliptical Micro-strip Patch Antenna (EMPA). The DEMPA is made out of a Double Elliptical Patch (DEP) which is designed as a combination of two half-elliptical patches either with a common minor axis and two different semi-major axes or with a common major axis and two different semi-minor axes. There are only two design parameters for an EMPA, its semi-major axis and semi-minor axis, whereas a DEMPA has three because of either two different semi-major axes or two different semi-minor axes. A parametric study is required to understand the relationship among these three design parameters and antenna characteristics such as return loss, impedance, resonant frequency, and gain. The present work is a statistical study, using the concept of Design of Experiments (DOE), of the impact of these design parameters on the return loss at resonant frequency within the frequency band of 8.50 GHz-10.55 GHz which has been earmarked for radiolocation applications by regulating agency. The Central Composite Design (CCD) technique in the Response Surface Methodology (RSM) of DOE has been employed here to develop empirical relationship between the design parameters and response variable. Numerical models were developed using Ansoft's HFSS as per the design matrix provided by Minitab. The concept of DOE helped to establish statistically significant parametric relationship between the design parameters and antenna return loss with the minimum amount of design effort. The predictive ability of regression model was confirmed by using numerical models of two DEMPAs that were not utilized to build the empirical relationship, one among which had been fabricated, tested and reported in literature.

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Optimizing submerged arc welding using response surface methodology, regression analysis, and genetic algorithm

Cited by 52 publications

References 9 publications

Application of Multivariate Analysis Methods in Welding Engineering

Application of Multivariate Analysis Methods in Welding Engineering

Experimental studies of Regulated Metal Deposition (RMD™) on ASTM A387 (11) steel: study of parametric influence and welding performance optimization

Predicting Performance Characteristics of Double Elliptical Micro-Strip Patch Antenna for Radiolocation Applications Using Response Surface Methodology

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