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Abstract <p>The article presents an overview of modern approaches to modeling the parameters of the surface layer formed as a result of surface plastic deformation. Three key areas are considered: stress–strain modeling with prediction of residual stresses, numerical and analytical roughness modeling, and the use of artificial intelligence and machine learning methods to predict roughness parameters and control surface quality. Special attention is given to the potential of integrating digital models with adaptive control systems and digital twins. A comparative analysis of the methods, their accuracy, limitations, and development prospects is performed. The importance of hybrid approaches that combine physical modeling and AI for improving design efficiency and optimizing hardening processes is emphasized.</p>

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Modern Approaches to Modeling the Parameters of the Surface Layer after Surface Plastic Deformation

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • P. A. Mel’nikov

摘要

Abstract

The article presents an overview of modern approaches to modeling the parameters of the surface layer formed as a result of surface plastic deformation. Three key areas are considered: stress–strain modeling with prediction of residual stresses, numerical and analytical roughness modeling, and the use of artificial intelligence and machine learning methods to predict roughness parameters and control surface quality. Special attention is given to the potential of integrating digital models with adaptive control systems and digital twins. A comparative analysis of the methods, their accuracy, limitations, and development prospects is performed. The importance of hybrid approaches that combine physical modeling and AI for improving design efficiency and optimizing hardening processes is emphasized.