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Abstract <p>Several approaches to solving metal forming problems using machine learning methods and deep learning algorithms are considered. An analysis of practical examples is performed. The relevance of using new algorithms and methods for solving metal forming problems in combination with SAE programs is shown. In conclusion of the article, an example is presented illustrating the obtained neural network model of AMg2 alloy flow stress, which allows predicting the flow stress value for pre-set combinations of temperature, deformation and deformation rate values.</p>

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An Overview of Forming Problems Solved with Machine and Deep Learning

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • P. A. Petrov

摘要

Abstract

Several approaches to solving metal forming problems using machine learning methods and deep learning algorithms are considered. An analysis of practical examples is performed. The relevance of using new algorithms and methods for solving metal forming problems in combination with SAE programs is shown. In conclusion of the article, an example is presented illustrating the obtained neural network model of AMg2 alloy flow stress, which allows predicting the flow stress value for pre-set combinations of temperature, deformation and deformation rate values.