Software to Predict the Process Parameters of Electron Beam Welding

This paper discusses the problem of choosing the effective process parameters of electron beam welding (EBW). To that end, the research team has developed a mathematical model that applies machine learning to predict the effective process parameters. Since predicting process parameters requires a regression model, this research uses regression analysis algorithms such as the ridge regression and the random forest regressor. The paper analyzes whether these algorithms are applicable to the problem and tests the accuracy of their predictions. To generalize the approach and strengthen the justification of choosing the hyperparameters of the regression algorithms studied herein and considering the high variability of these hyperparameters, the multiobjective optimization technique applicable for this combinatorial problem - an (evolutionary) genetic algorithm - is proposed to determine effective sets of hyperparameters. All the models successfully addressed the task, achieving a forecasting accuracy of at least 89%. The article presents the final form of the ridge regression model describing the dependence of the weld’s depth and width: for the weld depth, there is a 2nd degree polynomial dependence with a regularization of 10
⁻⁵
, and for the weld width, there is a 3rd degree polynomial dependence with a regularization of 10
⁻⁴
. An automated system based on this approach that accurately predicts the process parameters is proposed herein. In addition to performing basic modeling functions, the proposed system allows the visualization of the model-predicted data in the form of an interactive plot. This function could be useful for technologists by allowing them to determine the process parameters that ensure the required weld dimensions. Adopting the proposed EBW parameter prediction method in practice will provide decision support for cases when engineers need to test the EBW process or to start making new products.