Software to Predict the Process Parameters of Electron Beam Welding

Vadim S Tynchenko,Sergei V Tynchenko,Vladimir V Bukhtoyarov,Kirill A Bashmur,Vladislav V Kukartsev,Valeria V Tynchenko,Roman B Sergienko,Sergei O Kurashkin

doi:10.1109/access.2021.3092221

Vadim S Tynchenko, Sergei V Tynchenko + Show 6 more

Open Access

https://doi.org/10.1109/access.2021.3092221

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Abstract

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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> , and for the weld width, there is a 3rd degree polynomial dependence with a regularization of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> . 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.

Highlights

The processes of making permanent joints in various aerospace products using electron beam welding, induction soldering, or diffusion bonding are based on reusingThe associate editor coordinating the review of this manuscript and approving it for publication was Imran Sarwar Bajwa .preparation and equipment operation parameters that have been well tested before.an important part of the process consists of calibrating such parameters and testing them to see whether the results are reproducible and the final products are reliable
MATERIALS AND METHODS The input data were collected from experiments conducted to improve the process of making an electron beam welding (EBW) product comprising two parts of nonhomogeneous materials
The EBW unit involved in the experiments was designed to provide deep vacuum electron beam welding of assemblies made of stainless steel, titanium, aluminum, or special alloys

Summary

Introduction

An important part of the process consists of calibrating such parameters and testing them to see whether the results are reproducible and the final products are reliable. Tyncheko et al.: Software to Predict Process Parameters of EBW staff needs to calibrate and test a new process and to conduct pilot studies to find the parameters that will make a product of suitable quality. These are costly steps to take as they take some equipment runtime and working hours on the part of process designers and process testing staff. Each experiment will and knowingly result in destroying a specimen when testing the quality of permanent joints

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