Electron beam weld penetration depth prediction improved by beam characterisation
نویسندگان
چکیده
Abstract Predicting the penetration depth during electron beam welding (EBW) is important, but accuracy of current predictive models highly varied, depending on type and number data used. This paper develops compares several prediction for EBW uniquely influence used, as well measurement modelling methods. Although accelerating voltage, speed are essential inputs, additional focal position shape, measured using a novel 4-slit probing method, greatly improve predictions based an empirical equation, second-order regression artificial neural network (ANN). Optimised predict weld depths that deviate, average, by less than 5% from depths, valid very broad linear power density ranges (86–324 J/mm) close to estimated 4% inherent variability in process its measurement. Within this range, ANN yields accurate reliable predictions, demanding few 36 trials, decreasing required do not consider same targeted accuracy, more 60%. Adding large volumes virtual generated analytical or did capability developed study.
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ژورنال
عنوان ژورنال: The International Journal of Advanced Manufacturing Technology
سال: 2022
ISSN: ['1433-3015', '0268-3768']
DOI: https://doi.org/10.1007/s00170-022-10682-6