The article provides a comprehensive analysis of strategies, technologies, and organizational models for the maintenance and repair (MRO) of thermal processing equipment within the context of continuous production cycles typical for metallurgical, chemical, and energy industries. The features of continuous production (straight-line flow, system interdependence, powerful substance and energy flows, high downtime cost, territorial localization) imposing specific requirements on the MRO system are considered. A comparative analysis of classic organizational approaches — preventive maintenance, condition-based maintenance, and run-to-failure maintenance — is conducted, justifying the necessity for a flexible, adaptive hybrid model suited for continuous processes. Modern diagnostic methods applied for predictive maintenance and specifics of repairing critical units of thermal equipment under strict time constraints of planned shutdowns are detailed. Special focus is placed on integrating digital technologies (digital twins, IIoT, AI) as tools for synchronizing material and maintenance flows, minimizing downtime, and maximizing the efficiency of shutdown windows. It is concluded that the optimal MRO system for continuous production must be dynamic, data-driven, and combine principles of predictive maintenance with rigorous scheduling to ensure process continuity and economic efficiency.

The article is devoted to a comprehensive analysis of modern approaches to improving the efficiency and quality of parts processing on milling group machines with computer numerical control (CNC). The key factors affecting accuracy and productivity are considered: strategies for selecting cutting conditions, features of processing hard-to-machine materials (using stainless steels as an example), optimization of control programs (CP) using modern CAD/CAM systems, as well as issues of vibration resistance and dynamic rigidity of the technological system. A comparative analysis of various types of CNC milling machines, their layout and functionality is carried out. Special attention is paid to practical aspects of production preparation, including 3D modeling, selection of tool trajectory and adaptation of cutting conditions to the capabilities of medium-sized equipment. It is concluded that the maximum effect is achieved through the integration of precision equipment, advanced tooling solutions, intelligent programming and system analysis of the entire machining process.

The article places particular emphasis on the theoretical foundations of datuming and dimensional chains. Datuming theory explains the principles of stable component fixation, ensuring positional stability and eliminating possible shifts during machining, which is critical for achieving specified accuracy. Dimension chain theory enables a systematic approach to tolerance distribution, minimizing error accumulation and ensuring design parameters meet design requirements. An examination of the application of these theoretical principles to process design demonstrates how scientific knowledge can be integrated into practical production, improving the quality of manufactured devices. The paper highlights current trends in the development of instrument-making technology, including the digitalization of production processes, the implementation of additive manufacturing, and automated quality control. Key issues related to materials, fixtures, and productivity improvements, as well as prospects for their solution, are analyzed. The practical significance of the work lies in the potential use of the obtained theoretical and technological information for the development and optimization of production processes in instrument-making enterprises. This helps reduce defects, increase productivity, and improve the performance characteristics of products.

This paper presents a comprehensive study of the application of artificial intelligence technologies to address applied challenges in modern mechanical engineering, with a focus on the development of a prototype automatic visual inspection system. Based on an analysis of current scientific and industry literature, an analytical review of key areas of AI implementation in mechanical engineering was conducted. It was established that AI has evolved from an automation tool to a key component of intelligent manufacturing systems, capable of solving cognitive problems and enabling the transition to predictive and adaptive control models. A systematic classification of mechanical engineering problems solved using AI was developed and presented, structured by stages of the product lifecycle (PLM). This clearly demonstrates the broad impact of this technology — from generative design and predictive maintenance to logistics optimization and service analytics. A detailed analysis of existing commercial solutions and successful implementation cases was conducted. It was revealed that predictive maintenance (PdM) and automatic visual inspection (AVI) systems based on computer vision are the most mature and cost-effective. A comparison of platforms from leading vendors (Siemens, Autodesk, Dassault Systèmes) showed a trend toward deep integration of AI into unified PLM ecosystems, creating digital, continuous data flows.

Modern machine building imposes high requirements on the efficiency, reliability and resource of metalworking equipment. The report is devoted to a comprehensive analysis of lathe capabilities, including their classification, structural elements, technological processing parameters and factors affecting accuracy and productivity. Mechanisms of wear of key units, CNC control systems, diagnostic and predictive maintenance methods, as well as economic aspects of operation are considered. Special attention is paid to modern directions for increasing equipment resource based on Industry 4.0 digital technologies, innovative materials and work mode optimization. Based on the analysis, promising approaches to extending machine life and reducing operating costs are presented.

The article presents a systematic analysis of modern materials used in instrument engineering. Traditional structural materials (steels, aluminum and titanium alloys), polymers and composites, as well as special functional materials: piezoelectrics, magnetostrictive, thermoelectric and shape memory alloys are considered. An assessment of their key properties - mechanical, technological, operational — is given. Special attention is paid to innovative materials (carbon nanocomposites, metal foams, self-healing polymers) and trends in instrument engineering materials science: multifunctionality, adaptability, intellectualization and environmental friendliness. In conclusion, the thesis is substantiated that progress in instrument engineering is directly determined by the development of materials science, and the future of the industry is associated with the creation of hybrid and "smart" materials that ensure a qualitative leap in device characteristics.

This article is devoted to analyzing the causes of component wear during operation and systematizing contemporary restoration methods. Both traditional and innovative technologies are examined — technologies that not only restore a part’s functionality but, in some cases, even enhance its operational performance beyond that of the original. Particular attention is paid to the criteria for selecting the optimal restoration method, taking into account structural, technological, and economic factors.