Russian Federation
This article explores digital twins as an innovative tool for optimizing industrial production process-es. It presents the theoretical foundations of the technology, including the definition and structure of a digital twin, methods for its creation, and integration with modern digital production manage-ment systems. Special attention is given to the applications of digital twins in mechanical engineer-ing, energy, and the chemical industry. The study analyzes modeling and forecasting algorithms used to enhance equipment productivity and reliability. Examples of successful technology imple-mentations in real enterprises are provided, demonstrating economic benefits such as cost reduction, minimization of downtime, and improved resource utilization. The limitations of digital twins are discussed, including technical barriers and high implementation costs, as well as the prospects for technological development in the context of industrial digitalization and the industry 4.0 concept.
digital twins, production process optimization, machine learning, modeling, Internet of Things, in-dustrial automation, predictive analytics, Industry 4.0, productivity, industrial equipment
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