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Abstract <p>An overview of the application of artificial intelligence and process control models in mechanical engineering is presented. A number of key aspects are considered, including increasing productivity, product quality, and predicting equipment failures, as well as the prospects and problems of using artificial intelligence in this area. Integration issues and security concerns related to the implementation of artificial intelligence are discussed. Particular attention is paid to the problems of development and application of models of technological process control to improve production efficiency. The role of artificial intelligence in this process is emphasized, and its potential for automation and intellectualization of management is noted. Several important directions for further research are proposed, including the development of more complex and adaptive models, exploration of the possibilities of integrating machine learning methods, conducting experiments in real production conditions, and developing standards for evaluating the effectiveness of control models.</p>

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Development of Models for Managing Technological Processes for Production of Products of Mechanical Engineering

  • 期刊论文

  • 发表日期：

    2025年12月9日

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  • M. N. Erofeev,
  • I. N. Kravchenko,
  • M. V. Kryukov,
  • S. I. Nekrasov

摘要

Abstract

An overview of the application of artificial intelligence and process control models in mechanical engineering is presented. A number of key aspects are considered, including increasing productivity, product quality, and predicting equipment failures, as well as the prospects and problems of using artificial intelligence in this area. Integration issues and security concerns related to the implementation of artificial intelligence are discussed. Particular attention is paid to the problems of development and application of models of technological process control to improve production efficiency. The role of artificial intelligence in this process is emphasized, and its potential for automation and intellectualization of management is noted. Several important directions for further research are proposed, including the development of more complex and adaptive models, exploration of the possibilities of integrating machine learning methods, conducting experiments in real production conditions, and developing standards for evaluating the effectiveness of control models.