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<p>In this paper, a computer simulation of round wire drawing processes with different equations of state for steel A12 has been carried out. In addition, the methods of improving the configuration of neural networks based on a multilayer perceptron (MLP) for estimating the distributions of residual stress tensor components have been investigated. The study demonstrated that the strain rate exerted a significant influence on the character of the processes, particularly within the central region (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="170_2025_16757_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(0r-0.4r\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>0</mn> <mi>r</mi> <mo>-</mo> <mn>0.4</mn> <mi>r</mi> </mrow> </math></EquationSource> </InlineEquation>) of the investigated specimens. In addition, the employment of software tools for the purpose of tuning the hyperparameters of trained machine learning models, including Optuna, BayesianOpt, and Skopt, has been demonstrated to enhance the predictive capability of the models. Consequently, this results in an improvement in the accuracy of the obtained distributions of the required characteristics.</p>

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Forecasting the residual stress components in wires using an artificial neural network

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Dmitriy Demin,
  • Ilya Grebenkin,
  • Alexey Barinov

摘要

In this paper, a computer simulation of round wire drawing processes with different equations of state for steel A12 has been carried out. In addition, the methods of improving the configuration of neural networks based on a multilayer perceptron (MLP) for estimating the distributions of residual stress tensor components have been investigated. The study demonstrated that the strain rate exerted a significant influence on the character of the processes, particularly within the central region ( \(0r-0.4r\) 0 r - 0.4 r ) of the investigated specimens. In addition, the employment of software tools for the purpose of tuning the hyperparameters of trained machine learning models, including Optuna, BayesianOpt, and Skopt, has been demonstrated to enhance the predictive capability of the models. Consequently, this results in an improvement in the accuracy of the obtained distributions of the required characteristics.