Registration Number
0226U004349,
(0124U004247)
, R & D reports
Title
Development of intelligent control systems for optimization of machining and assembly processes in mechanical engineering.
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Розробка та тестування моделей: 2.1.Створення математичних моделей і алгоритмів, заснованих на сучасних принципах штучного інтелекту та машинного навчання. 2.2.Реалізація алгоритмів в програмному забезпеченні для комп'ютерного моделювання. 2.3.Проведення комп'ютерних симуляцій для тестування ефективності розроблених моделей. 2.4.Аналіз результатів тестувань та внесення необхідних корективів у моделі та алгоритми.
Head
Kovalevskyy Serhii V., Доктор технічних наук
Registration Date
11-08-2026
Organization
Donbass State Engineering Academy
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Development of intelligent control systems will include the use of artificial intelligence methods, particularly machine learning and neural networks, for the analysis of data from machining and assembly processes.
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Object of Research: Digital manufacturing systems for machining and assembly in mechanical engineering, developed on the basis of digital twin concepts, adaptive control, and the integration of cyber-physical technologies.
Subject of Research: Mathematical models, artificial intelligence algorithms, and software-algorithmic tools of intelligent control systems for prediction, optimization, and adaptive control of machining and assembly technological processes under conditions of a multifactor industrial environment.
Purpose of the Work: To develop and investigate mathematical models, algorithms, and software tools for a digital twin of a manufacturing system based on modern methods of artificial intelligence, machine learning, and predictive analytics in order to ensure adaptive control, multi-criteria optimization, and increased efficiency of machining and assembly processes in mechanical engineering.
Scientific Novelty: For the first time, a formalization of a manufacturing system as a multilevel state space of a digital twin with the integration of physically grounded models, intersystem interactions, and adaptive artificial intelligence components has been proposed. Hybrid mathematical models based on PINN and UDE approaches, online adaptation algorithms, tool wear prediction, system state estimation, and detection of emergent operating modes have been developed.
Field of Application: Mechanical engineering, digital manufacturing, Smart Manufacturing, cyber-physical production systems, predictive control systems, machining, automated assembly, digital twins of manufacturing processes.
Product Description
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Sergiy Kovalevskyy
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2026-08-11
