Statistical modelling and computer programs for optimisation of the electron beam welding of stainless steel

Koleva, Elena

doi:10.1016/s0042-207x(01)00274-3

Cited by 40 publications

(12 citation statements)

References 5 publications

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“…Koleva [14] has developed models to investigate the influence of electron beam welding (EBW) parameters namely electro beam power, welding velocity, distance from the main surface of the magnetic lens to the focus point and the distance between the magnetic lens and the sample surface on the welding depth and width. The experiment was performed with samples of austenitic steel, type 1H18NT.…”

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

366

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

366

128

View full text Add to dashboard Cite

show abstract

“…Koleva (53) has developed models to investigate the influence of Electron Beam Welding (EBW) parameters namely electro beam power, welding velocity, distance from the main surface of the magnetic lens to the focus point, and the distance between the magnetic lens and the sample surface on the welding depth and width. The experiment was performed with samples of austenitic steel, type 1H18NT.…”

Section: Weld Bead Geometrymentioning

confidence: 99%

Mathematical Modeling of Weld Phenomena, Part 2

Olabi

Alaswad

Benyounis

2014

Comprehensive Materials Processing

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“…Benyounis et al [12] adopted RSM to predict weld profile in laser-welded medium carbon steel. Koleva [13,14] used statistical regression analysis to develop the correlation of input parameters, namely beam power, welding velocity, distance of the job from focusing lens, and focus position with the weld parameters, i.e. depth and width for EBW of austenitic stainless steel.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on electron beam butt welding of austenitic stainless steel 304 plates and its input–output modelling using neural networks

Jha

Pratihar

Dey

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part B:

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Butt welding of austenitic stainless steel 304 plates was carried out using an electron beam. Experiments were conducted for various combinations of input process parameters determined according to central composite design. Three input parameters, namely accelerating voltage, beam current, and welding speed were considered during the experiments. The weld-bead parameters, namely bead width and its depth of penetration, and weld strength in terms of yield strength and ultimate tensile strength, were measured as the responses of the process. Inputoutput modelling of this process was carried out in the forwards direction using regression analysis, a back-propagation neural network (BPNN), and a genetic algorithm-tuned neural network (GANN). Reverse mapping of this process was also attempted using the BPNN and GANN-based approaches, although the same could not be done from the obtained regression equations. The GANN was found to outperform the other two approaches in forward mapping. In reverse mapping also, GANN was seen to perform better than BPNN. The reason for this could have been that the back-propagation algorithm of the BPNN was replaced by a genetic algorithm in the GANN.

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Statistical modelling and computer programs for optimisation of the electron beam welding of stainless steel

Cited by 40 publications

References 5 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

Mathematical Modeling of Weld Phenomena, Part 2

Study on electron beam butt welding of austenitic stainless steel 304 plates and its input–output modelling using neural networks

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