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

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.

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Publication Details

Journal
Journal of Machinery Manufacture and Reliability
Published
2025-12-01
DOI
https://doi.org/10.1134/s1052618825700347
Primary Topic
Engineering Technology and Methodologies
Type
article
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0.00
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article

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

И. Н. Кравченко, С. А. Некрасов, Михаил Ерофеев, M. V. Kryukov
Journal of Machinery Manufacture and Reliability
Engineering Technology and Methodologies
article

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

И. Н. Кравченко, С. А. Некрасов, Михаил Ерофеев, M. V. Kryukov
article en

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.

Journal of Machinery Manufacture and ReliabilityVol. 54(7)
Moscow State Agroengineering University named after V.P. Goryachkin (RU), Mechanical Engineering Research Institute of the Russian Academy of Sciences (RU)
Openalex Percentile: Top 49%
Engineering Technology and Methodologies
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