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The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.