Electron beam weld parameters and thermal efficiency improvement

Koleva, Elena

doi:10.1016/j.vacuum.2004.09.002

Cited by 44 publications

(17 citation statements)

References 3 publications

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“…Koleva [20] has carried out another work by applying RSM to establish the relationship between performance characteristics (weld depth, weld width and thermal efficiency) and its influencing factors (beam power, welding velocity, focus position, focusing current of the beam and the distance to the sample surface) for EBW of austenitic stainless steel. Optimal welding regimes were found through the thermal efficiency optimization.…”

Section: Response Surface Methodologymentioning

confidence: 99%

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Benyounis

Olabi

2008

Advances in Engineering Software

359

128

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Welding input parameters play a very significant role in determining the quality of a weld joint.The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion.Nowadays, application of Design of Experiment (DoE), Evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein.This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds.

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Section: Response Surface Methodologymentioning

confidence: 99%

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Benyounis

Olabi

2008

Advances in Engineering Software

359

128

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“…Electron beam spot size determines the energy density of the electron beam, which is a very important factor for controlling electron beam welding penetration [38]. Many investigators have studied the effects that electron beam welding parameters have on the depth of penetration [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. The first mathematical relationship to predict penetration depth from welding parameters was developed in 1965 [38,49].…”

Section: Selection Of Welding Parameters and Prediction Of Weld Geometrymentioning

confidence: 99%

Electron beam welding of copper‐niobium microcomposites for pulsed power applications

Višniakov

Mikalauskas

Černašėjienė

et al. 2018

Materialwissenschaft Werkst

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The main objective of this work is to explore the possibility of applying electron beams to connect copper‐niobium conductors. Electron beam welding of copper–niobium microcomposite wires was investigated. The evaluation of welded joint properties was carried out according to the same methodology applied to contact electrical connections. The major electrical and mechanical properties of welded joints were established. The microscopic examination of the joint cross‐section showed that welded joints of copper‐niobium conductors have minimal thermal effects on the structure of the conductor and on the propagation of welding defects thanks to the welding in a vacuum. According to the non‐destructive radiographic test, the joint structure does not have welding defects. The difference in electrical resistances of the conductor and welded joint was below 20 %. The welded joint can withstand the maximum load, which is equal to 31.25 % of the load‐bearing capacity of microcomposite conductor.

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“…Benyounis et al 17 used response surface methodology (RSM) successfully to predict weld pro¯le in laser welding. The regression analysis was used by Elena Koleva 18,19 and Dey et al 20 to develop the input-output correlations for electron beam welding of austenitic stainless steel.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Input-Output Relationships for Electron Beam Butt Welding of Dissimilar Materials Using Neural Networks

Jha

Pratihar

Bapat

et al. 2014

Int. J. Comp. Intel. Appl.

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Electron beam butt welding of stainless steel (SS 304) and electrolytically tough pitched (ETP) copper plates was carried out according to central composite design of experiments. Three input parameters, namely accelerating voltage, beam current and weld speed were considered in the butt welding experiments of dissimilar metals. The weld-bead parameters, such as bead width and depth of penetration, and weld strength in terms of yield strength and ultimate tensile Int. J. Comp. Intel. Appl. 2014.13. Downloaded from www.worldscientific.com by UNIVERSITY OF AUCKLAND LIBRARY -SERIALS UNIT on 10/13/14. For personal use only. strength were measured as the responses of the process. Input-output relationships were established in the forward direction using regression analysis, back-propagation neural network (BPNN), genetic algorithm-tuned neural network (GANN) and particle swarm optimization algorithm-tuned neural network (PSONN). Reverse mapping of this process was also conducted using the BPNN, GANN and PSONN approaches, although the same could not be done from the obtained regression equations. Neural networks were found to tackle the problems of both forward and reverse mappings e±ciently. However, neural networks tuned by the genetic algorithm and particle swarm optimization algorithm were seen to perform better than the BPNN in most of the cases but not all.

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Electron beam weld parameters and thermal efficiency improvement

Cited by 44 publications

References 3 publications

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Electron beam welding of copper‐niobium microcomposites for pulsed power applications

Modeling of Input-Output Relationships for Electron Beam Butt Welding of Dissimilar Materials Using Neural Networks

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