Перегляд за автором "Khalimonov, Y. I."

Зараз показано 1 - 3 з 3
  • Зображення мініатюри
    Публікація
    Circular economy in automated systems
    (ХНУРЕ, 2025) Khalimonov, Y. I.; Sotnik, S. V.
    Circular economics in automated transfer systems are moving from a linear model to cyclical development of resources related to technologies: SHI, IoT, robotics, blockchain and 3D tools. They will optimize production, automate the processing of products, ensure the transparency of supply lines and the production of products with a development for future disposal. The price of the world's most important is the cost of new economic opportunities.
  • Зображення мініатюри
    Публікація
    Monitoring and optimising conditions in production environment
    (Видавництво ОНТУ, 2024) Khalimonov, Y. I.; Sezonova, I. K.; Sotnik, S. V.
    The study explores introduction of IoT sensor technologies aimed at maintaining optimal conditions within production environment. It investigates contemporary approaches to developing integrated monitoring systems capable of tracking essential production parameters, including air quality and energy consumption levels. A comprehensive analysis is provided on existing challenges in implementing these systems, covering technological, infrastructural, and economic aspects. Emphasis is placed on the integration of various sensor types into unified network to ensure reliable data transmission. A systematic approach is proposed for evaluating and addressing these challenges, enabling more efficient adoption of IoT solutions in production. The findings offer practical insights for modernization of industrial facilities and creation of new systems for workplace condition monitoring.
  • Зображення мініатюри
    Публікація
    Overview of computer vision areas application for inspection and quality control
    (ХНУРЕ, 2024) Khalimonov, Y. I.; Sotnik, S. V.
    The work discusses the integration of computer vision technologies into modern production processes, focusing on their role in automating inspection and quality control. By leveraging high-resolution cameras, lighting systems, and advanced image processing algorithms, these systems ensure consistent product quality, reduce manual labor, and minimize the impact of human error. Key functionalities include surface defect detection (e.g., scratches, cracks), dimensional accuracy measurement, assembly verification, and shape analysis. Advanced methods like 3D scanning and machine learning are highlighted as pivotal for detecting defects with high precision and adapting to dynamic production environments. Additionally, the integration with Big Data and manufacturing solutions (e.g., ERP and MES) facilitates efficient quality data management. The work emphasizes that computer vision systems enhance production efficiency, cost-effectiveness, and product reliability, making them indispensable tools in modern manufacturing. It concludes by noting the technology's growing importance for professionals in automation and robotics, offering vast opportunities for innovation and research.