Optimization of bead geometry in electron beam welding using a Genetic Algorithm

Dey, Vidyut; Pratihar, Dilip Kumar; Datta, G. L.; Jha, M. N.; Saha, Tushar; Bapat, A.V.

doi:10.1016/j.jmatprotec.2008.03.019

Cited by 99 publications

(30 citation statements)

References 13 publications

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“…The literature review indicates that previous research papers and journals in this field [4,5,9] have not included the effect of focusing current (F) an input variable. The inclusion of focusing current as an influential parameter further complicated the selection of levels for full factorial design of experiment.…”

Section: Effect Of Focusing Currentmentioning

confidence: 99%

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Prediction and optimization of weld bead geometry for electron beam welding of AISI 304 stainless steel

Siddaiah

Singh

Mastanaiah

2016

Int J Adv Manuf Technol

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Electron beam welding, though considered a sophisticated welding process, still requires the operator to first carry out several trial welds to find the right combination of welding parameters based on intuition and experience. This archaic method is often unreliable, leading to unproductive manufacturing lead time, man hours, quality control tests, and material wastage. The current study eliminates this "trial and error" method by providing a reliable model which can predict the right combination of weld parameters to achieve a high-quality weld. Beads on plate welds were carried out on AISI 304 stainless steel plates using a low-kilovolt electron beam welding (EBW) machine. A model that can predict weld bead geometry and provide optimized output for minimum weld area condition without compromising on weld quality was developed. Experimental data were collected as per full factorial design of experiments, and the levels for each input parameter were established through pilot experiments. A multivariate regression analysis has been conducted to establish a relationship between four weld input parameters (three levels each) and four weld bead responses. Response surface methodology (RSM) has been used to study the interrelationship between input parameters and their effect on each response variable. Further, minimization of weld cross-sectional area was done using genetic algorithm for maximum penetration and minimum weld area condition. The optimized mathematical model convincingly establishes that the focusing current is a significant input parameter with very high influence over the weld bead geometry. Extensive material characterization and mechanical tests have been carried out to validate the regressed input-output relationship and the optimized mathematical model. Keywords Electron beam welding . Weld bead geometry . Stainless steel . Full factorial design of experiment . Multiple regression analysis . Response surface methodology Abbreviations ANOVA Analysis of variance BBW Back bead width BH Bead height BP Bead penetration BW Bead width D Standoff distance/work to chamber top distance (mm) EBW Electron beam welding F Focusing current (A) HAZ Heat-affected zone I Beam current (mA) RSM Response surface methodology R-sq, R 2 Coefficient of correlation S Welding speed/beam spot travel (m/min) SE Standard error T Thickness of plate (mm) V Accelerating voltage (kV) * Arpith Siddaiah

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Section: Effect Of Focusing Currentmentioning

confidence: 99%

“…Dey [4,5] conducted a study for bead-on-plate electron beam welding of 5-mm-thick austenitic stainless steel plates of ASS-304 grade and aluminum plates of Al-1100 grade. The primary aim of his study was to minimize weldment area by applying genetic algorithm optimization without sacrificing the quality of the weld.…”

Section: Introductionmentioning

confidence: 99%

Prediction and optimization of weld bead geometry for electron beam welding of AISI 304 stainless steel

Siddaiah

Singh

Mastanaiah

2016

Int J Adv Manuf Technol

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“…Kumaran et al [29,30] optimised a tube to tube plate using an external tool by genetic algorithms and Taguchi method. Dey et al [31] conducted the optimisation of bead geometry in electron beam welding using a GA. A binary-coded genetic algorithm with a penalty term was used to solve the said problem. Prediction of tensile strength and optimisation of process parameters for friction stir-welded dissimilar aluminium alloys [32,33] and stainless steel [34] were also conducted in the papers.…”

Section: Optimisation Of Welding Parametersmentioning

confidence: 99%

Effect of friction welding parameters on the tensile strength and microstructural properties of dissimilar AISI 1020-ASTM A536 joints

Winiczenko

2015

Int J Adv Manuf Technol

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This paper presents an effect of friction welding parameters on the tensile strength and microstructural properties of dissimilar AISI 1020-ASTM A536 joints. A hybrid response surface methodology (RSM) and genetic algorithm (GA)-based technique were successfully developed to model, simulate, and optimise the welding parameters. Direct and interaction effects of process parameters on the ultimate tensile strength (UTS) were studied by plotting graphs. Friction force and friction time have a positive effect on tensile strength. As friction force and friction time increase, the tensile strength also increases. The maximum tensile strength of the friction-welded low carbon steel-ductile iron joints was 87 % of that of the base metal. The tensile properties, microstructure, Vickers hardness distribution, and fracture morphology of the welded specimen have been studied and presented in this study. Additionally, the distribution of carbon element on both sides of the interface was estimated using energydispersive spectroscopy (EDS). The results of the metallographic study show clearly that the friction welding process was accompanied by a diffusion of carbon atoms from ductile iron to steel. This process causes the formation of a carbonrich zone at the interface and decarburization zone in the ductile iron close to the bond interface.

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“…The proposed GA manages to locate near optimum conditions, with a relatively small number of experiments. Vidyut Dey et al 9 developed a model to minimize the weldment area, after satisfying the condition of maximum bead penetration. Bead-on-plate weld runs were performed at an electron beam welding setup.…”

Section: Weldability Studies and Parameter Optimization Of Aisi 904lmentioning

confidence: 99%

Weldability studies and parameter optimization of AISI 904L super austenitic stainless steel using friction welding

et al. 2014

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Welding input parameters play a very significant role in determining the quality of a weld joint. The quality of the joint can be defined in terms of mechanical properties, distortion and weld-bead geometry. Generally, all welding processes are employed with the aim of obtaining a welded joint with the desired characteristics. The purpose of this study is to propose a method to decide near optimal settings for the welding process parameters in friction welding of (AISI 904L) super austenitic stainless steel by using non conventional techniques and genetic algorithm (GA). Grey relational analysis and the desirability approach were applied to optimize the input parameters by considering multiple output variables simultaneously. An optimization method based on genetic algorithm was then applied to resolve the mathematical model and to select the optimum welding parameters. The main objective of this work is to determine the friction welding process parameters to maximize the fatigue life and minimize the width of the partial deformation zone (left & right) and welding time. This study describes how to obtain near optimal welding conditions over a wide search space by conducting relatively a smaller number of experiments. The optimized values obtained through these evolutionary computational techniques were also compared with experimental results. ANOVA analysis was carried out to identify the significant factors affecting fatigue strength, welding time and partially deformed zone and to validate the optimized parameters.

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Optimization of bead geometry in electron beam welding using a Genetic Algorithm

Cited by 99 publications

References 13 publications

Prediction and optimization of weld bead geometry for electron beam welding of AISI 304 stainless steel

Prediction and optimization of weld bead geometry for electron beam welding of AISI 304 stainless steel

Effect of friction welding parameters on the tensile strength and microstructural properties of dissimilar AISI 1020-ASTM A536 joints

Weldability studies and parameter optimization of AISI 904L super austenitic stainless steel using friction welding

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