Registration Number
0226U004395,
(0124U004247)
, R & D reports
Title
Development of intelligent control systems for optimization of machining and assembly processes in mechanical engineering.
popup.stage_title
Аналіз існуючих систем і формування вимог до нової системи: 1.1.Проведення детального аналізу існуючих технологій і методів оптимізації технологічних процесів механообробки та складання. 1.2.Визначення ключових проблем і недоліків, які потребують удосконалення. 1.3.Формулювання технічних вимог та цілей для розробки нових інтелектуальних систем управління. 1.4.Розробка початкових технічних специфікацій для подальшого проектування моделей.
Head
Kovalevskyy Serhii V., Доктор технічних наук
Registration Date
26-08-2026
Organization
Donbass State Engineering Academy
popup.description1
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.
popup.description2
. 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
popup.authors
Serhii V. Kovalevskyi
Serhii H. Onyshchuk
Oleksandr O. Pelypynko
Pavlo V. Nadich
Oleksandr Y. Nykolaienko
Olena Kovalevska
Darya Sidyuk
Ilya Bakhtin
Ivan Bezrodniy
Alexey Grebenkin
Mykyta Sokolov
popup.nrat_date
2026-08-26
