10.34: Creep properties of grade S275JR steel at high temperature

Torić, Neno; Brnić, Josip; Boko, Ivica; Čanađija, Marko; Turkalj, Goran; Lanc, Domagoj; Brčić, Marino; Burgess, Ian; Harapin, Alen; Divić, Vladimir; Uzelac, Ivana

doi:10.1002/cepa.331

Cited by 2 publications

(2 citation statements)

References 6 publications

(10 reference statements)

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“…Alloy steel grade S275JR was used as the substrate material, with dimensions of 100 x 75 x 20 mm, and its chemical composition is shown in Table 1 [18]. Two groups of bead-on-plate welds were produced.…”

Section: Signal Analysismentioning

confidence: 99%

Electron Beam Weld Penetration Depth Prediction Improved by Beam Characterization

Yin

Kennedy²,

Mitchell³

et al. 2022

Preprint

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The influence of beam characterization on methods to predict the penetration depth during electron beam welding (EBW) is studied, including an empirical equation, a second-order regression and an artificial neural network (ANN). A 4-slits beam probing method is introduced and applied to calibrate beam radius and focal position during EBW. Where possible the input types were varied and their effect on the accuracy of the model prediction was compared. In addition to accelerating voltage, beam current and welding speed, detailed measurement of beam radius and focal position greatly improves the accuracy of the model predictions. The data of detected beam radius and focal position can reduce the mean absolute deviations to as low as 4.5% and close to the estimated 4% inherent variability in the process. Beam characterization also enabled the ANN to provide accurate and reliable depth prediction with a minimum of 36 data points, which avoids redundant trial and error tests before EBW processes.

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Section: Signal Analysismentioning

confidence: 99%

Electron Beam Weld Penetration Depth Prediction Improved by Beam Characterization

Yin

Kennedy²,

Mitchell³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Alloy steel grade S275JR was used as the substrate material, with dimensions of 100 × 75 × 20 mm. Its chemical composition is shown in Table 1 [26]. Two groups of bead-on-plate welds were produced.…”

Section: Welding Experimentsmentioning

confidence: 99%

Electron beam weld penetration depth prediction improved by beam characterisation

Yin

Kennedy

Mitchell

et al. 2022

Int J Adv Manuf Technol

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Predicting the penetration depth during electron beam welding (EBW) is important, but the accuracy of current predictive models is highly varied, depending on the type and number of data used. This paper develops and compares several penetration depth prediction models for EBW and uniquely compares the influence of the number and type of data used, as well as the measurement and modelling methods. Although accelerating voltage, beam current and welding speed data are essential modelling inputs, additional data for beam focal position and beam shape, measured using a novel 4-slit beam probing method, greatly improve the accuracy of predictions for models based on an empirical equation, a second-order regression and an artificial neural network (ANN). Optimised models predict weld depths that deviate, on average, by less than 5% from measured depths, are valid for very broad linear electron beam power density ranges (86–324 J/mm) and are close to the estimated 4% inherent variability in the process and its measurement. Within this linear electron beam power density range, the ANN yields accurate and reliable depth predictions, demanding as few as 36 welding trials, decreasing the number required for models that do not consider beam focal position and shape, for the same targeted accuracy, by more than 60%. Adding large volumes of virtual data generated by less reliable analytical or regression models did not improve the predictive capability for the ANN developed in this study.

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10.34: Creep properties of grade S275JR steel at high temperature

Cited by 2 publications

References 6 publications

Electron Beam Weld Penetration Depth Prediction Improved by Beam Characterization

Electron Beam Weld Penetration Depth Prediction Improved by Beam Characterization

Electron beam weld penetration depth prediction improved by beam characterisation

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