CC BY
1UDC. 621
INVESTIGATING TECHNOLOGICAL SYSTEM STRUCTURES IN METAL PROCESSING FOR ENHANCED PRODUCTIVITY AND RELIABILITY
Tuyboyov Oybek Valijonovich, PhD, dotsent. Toshkent davlat texnika universiteti, E-mail: oybektuyboyov85@gmail.com
Toshtemirov Kamol Qahramonovich, katta o'qituvchi, Toshkent davlat texnika universiteti,
Olmaliq filiali, E-mail: to shtemirovkamol7 @gmail.com
Toshtemirova Gulnora Ayubjonovna, assistant, Toshkent davlat texnika universiteti,
Olmaliq filiali,
E-mail: gulnoratoshtemirovamt@gmail.com
Annotatsiya. Bu maqolada tegishli tadqiqotlarni keng ko'lamli ko'rib chiqish orqali monitoring va diagnostika tizimlari, jarayonni boshqarish, optimallashtirish va tizimli murakkablikni baholash kabi turli jihatlar o'rganish natijalari yoritilgan. Raqamli texnologiyalar, jumladan, internet ma'lumotlari (IM), sun'iy intellekt (SI) va ilg'or ma'lumotlar tahlili integratsiyasi metallarni qayta ishlash operatsiyalarida o'zgartiruvchi kuchga aylanishi sabab raqamlashtirish jarayonlarni optimallashtirish, samaradorlikni oshirish va avtomatlashtirish, prognozli texnik xizmat ko'rsatish va real vaqtda monitoring orqali sifatni ta'minlash imkoniyatlarini ochib berilgan. Bundan tashqari, ishonchlilik nazariyasini jarayonni takomillashtirish tashabbuslariga kiritish xavfni kamaytirish va samaradorlikni optimallashtirish uchun proaktiv yondashuvlar orqali ifodalangan. Muvaffaqiyatga erishish ehtimolini muntazam ravishda baholash va ishonchlilikka ta'sir qiluvchi muhim omillarni aniqlash orqali tashkilotlar samaradorlik va ishonchlilikni samarali oshirish uchun maqsadli chora-tadbirlarni ishlab chiqishi mumkin.
Аннатация. Путем обширного обзора соответствующих исследований мы изучаем различные аспекты, такие как системы контроля и диагностики, управление процессами, оптимизация и оценка структурной сложности. Интеграция цифровых технологий, включая интернет вещей (ИВ), искусственный интеллект (ИИ) и передовую аналитику данных, становится преобразующей силой в операциях по обработке металлов. Цифровизация открывает возможности для оптимизации процессов, повышения эффективности и обеспечения качества за счет автоматизации, профилактического обслуживания и мониторинга в реальном времени. Кроме того, включение теории надежности в инициативы по улучшению процессов представляет собой упреждающий подход к снижению рисков и оптимизации производительности. Систематически, оценивая вероятность успеха и выявляя критические факторы, влияющие на надежность, организации могут разработать целевые меры для эффективного повышения производительности и надежности.
Abstract. Through an extensive review of relevant studies, we examine various aspects such as control and diagnostic systems, process management, optimization, and structural complexity evaluation. The integration of digital technologies, including Internet of Things (IoT), artificial intelligence (AI), and advanced data analytics, emerges as a transformative force in metal processing operations. Digitalization offers opportunities for streamlining processes, improving efficiency, and ensuring quality through automation, predictive maintenance, and
real-time monitoring. Additionally, the incorporation of reliability theory into process improvement initiatives presents a proactive approach to mitigating risks and optimizing performance. By systematically assessing the probability of success and identifying critical factors affecting reliability, organizations can develop targeted interventions to enhance productivity and reliability effectively.
Kalit so'zlar. metallga ishlov berish, texnologik tizimlar, raqamlashtirish, ishonchlilik nazariyasi, jarayonni optimallashtirish, mahsuldorlikni oshirish, nazorat qilish tizimlari, internet narsalari, sun'iy intellekt, barqaror rivojlanish.
Ключевые слова. металлообработка, технологические системы, цифровизация, теория надежности, оптимизация процессов, повышение производительности, системы управления, интернет вещей, искусственный интеллект, устойчивое развитие.
Keywords. Metal processing, Technological systems, Digitalization, Reliability theory, Process optimization, Productivity enhancement, Control systems, IoT, Artificial intelligence, Sustainability
Introduction. The research papers provide insights into investigating technological system structures in metal processing. Various studies delve into different aspects such as the development of control and diagnostic systems for metal structures [1], the process approach to technology management in manufacturing companies [2], optimization problems in technological systems with parallel kinematic structures [3], a metal tube processing technical system for efficient metal pipe processing [4], and a new approach to evaluating the complexity of technological systems in the mining and metallurgical complex [5]. These papers collectively contribute to understanding the intricacies of technological systems in metal processing, ranging from control and diagnostic methods to process management, optimization, and structural complexity evaluation, offering valuable insights for enhancing efficiency and quality in metal processing operations. Metal processing industries are crucial for economic development but face challenges like machinery maintenance delays [6], low productivity, and the need for environmental improvements. Total Productive Maintenance and Systematic Layout Planning can enhance productivity by up to 7.69% and reduce cycle time by 32 days. Implementing Lean Manufacturing techniques like 5's, TPM, Six Sigma, and Poka Yoke can lead to productivity improvements of 8 to 12% by reducing defects and downtime. Additionally, focusing on process standardization, Six Sigma, and total productive maintenance can result in a 2.5% reduction in losses and an 11% productivity increase in metallurgical SMEs . Metal processing plays a crucial role in enhancing productivity and reliability in manufacturing. Various methods such as forming, cutting, and coating are employed to improve the physical and chemical performance of metal components [7]. Mechanical devices are designed to prevent shaking during metal cutting processes, ensuring precision and efficiency [8]. Techniques involving rotating and moving raw materials, along with tool movements, enable versatile cutting and processing of metals in different directions, enhancing flexibility and accuracy [9]. To ensure the success of productivity enhancement initiatives in metal processing, a new method assesses the probability of success based on reliability and capability factors, highlighting the importance of human factors and time persistence in achieving desired performance gains [10]. These advancements in metal processing not only drive productivity but also meet the requirements for diversified appearances and extended reliability testing times [11]. Investigating technological system structures in metal processing is crucial for enhancing productivity and reliability [12]. Understanding the process approach to technology management (TMP) in manufacturing companies reveals a complex sequence of steps with economic evaluations, feedback loops, and repeated assessments, emphasizing the importance of learning sources and means in influencing TMP [13]. Systems analysis of technological systems in metallurgical plants highlights the hierarchical levels and links within the technological system, showcasing the flows of material, energy, and information for operations like hot rolling of steel sheets [14]. Implementing digitalization in metal tube
processing systems can significantly improve processing efficiency and quality, offering promising prospects for combined processing of steel and iron. By integrating reliability theory into process improvement projects, organizations can assess the probability of success and enhance productivity by addressing human factors and organizational capabilities effectively.
Digitalization offers a wealth of opportunities for streamlining processes, improving efficiency, and ensuring quality in metal processing operations. By harnessing technologies such as Internet of Things (IoT), artificial intelligence (AI), and advanced data analytics, metal processing facilities can achieve unprecedented levels of automation, predictive maintenance, and real-time monitoring. The integration of digital technologies into metal tube processing systems, for instance, holds significant promise. Not only can it enhance processing efficiency and quality, but it can also facilitate the seamless integration of steel and iron processing, optimizing the utilization of resources and minimizing waste. With digitalization, manufacturers can gain insights into process performance in ways that were previously unattainable, enabling them to identify bottlenecks, optimize workflows, and make data-driven decisions to drive continuous improvement. Moreover, the incorporation of reliability theory into process improvement initiatives represents another avenue for enhancing productivity and reliability in metal processing. By systematically assessing the probability of success and identifying critical factors affecting reliability, organizations can proactively mitigate risks and optimize performance. This approach underscores the importance of addressing not only technical aspects but also human factors and organizational capabilities in driving sustainable improvements. By embracing advancements in control and diagnostic systems, process management techniques, optimization strategies, and digitalization, metal processing facilities can overcome challenges, enhance productivity, and meet the demands of a rapidly evolving market landscape. Through collaborative research efforts and the adoption of holistic approaches to technology management, the industry can pave the way for a more sustainable and prosperous future.
Methods. To investigate the technological system structures in metal processing and address the challenges faced by metal processing industries, a comprehensive research approach is adopted. The methodology comprises several key components aimed at gaining insights into control and diagnostic systems, process management, optimization, and structural complexity evaluation. Case studies are employed to examine real-world applications of control and diagnostic systems, process management strategies, optimization methods, and structural complexity evaluation techniques in metal processing facilities. By analyzing specific cases, insights into the challenges faced, solutions implemented, and outcomes achieved are obtained, facilitating a deeper understanding of the practical implications of various approaches.
Data pertaining to machinery maintenance delays, productivity levels, environmental impacts, and other relevant metrics are collected from metal processing facilities. Quantitative analysis techniques, such as statistical analysis and modeling, are applied to identify patterns, trends, and correlations in the data. Qualitative analysis methods, including interviews and surveys, are also utilized to gather insights from industry professionals regarding their experiences and perspectives on technological system structures in metal processing. Figure. 1 displays the trend of machinery maintenance delays over a period of 12 months. The x-axis represents the months, while the y-axis represents the machinery maintenance delays in hours. Each point on the plot represents the machinery maintenance delays for a specific month. The blue line connects the data points, showing the variation in maintenance delays over time. The next subplot illustrates the trend of productivity levels over the same 12-month period. The x-axis represents the months, while the y-axis represents the productivity levels. Similar to the first subplot, each point on the plot corresponds to the productivity levels for a specific month. The red line connects the data points, indicating the variation in productivity levels over time. Analysing such data can help identify patterns, trends, and potential correlations, enabling insights into the operational efficiency and performance of the metal processing facility.
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Productivity Levels Over Time 1000,-,-1---1-,—
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Figure. 1 Machinery Maintenance Delays Over Time, Productivity Levels Over Time
Simulation and modeling techniques are employed to evaluate the performance of technological system structures in metal processing under different scenarios. Computational models are developed to simulate the behavior of metal processing systems, taking into account factors such as equipment configurations, process parameters, and environmental conditions. By simulating various scenarios, the impact of different control strategies, process management techniques, and optimization methods on system performance can be assessed.
Figure. 2 Simulation Results: Processing Time for Different Scenarios
Figure. 2 presents the simulation results depicting the processing time variations across different scenarios in a metal processing system. Each scenario represents a distinct configuration, process parameter setting, or optimization method applied within the system. The x-axis represents the five scenarios considered in the simulation, while the y-axis indicates the processing time in minutes. The box plot visualizes the distribution of processing times for each
scenario, providing insights into the variability and central tendency of the simulated data. The box represents the interquartile range (IQR) of the processing times for each scenario, with the median indicated by the horizontal line inside the box. The whiskers extend to the minimum and maximum values within 1.5 times the IQR from the first and third quartiles, respectively, capturing the range of the data distribution. Individual data points lying beyond the whiskers are considered outliers and are plotted individually as circles, highlighting any extreme values in the simulated processing times. By examining the box plot, one can assess the variability in processing times across different scenarios and identify potential trends or patterns. This analysis aids in understanding the effectiveness of various control strategies, process management techniques, and optimization methods in influencing system performance in metal processing.
The integration of digital technologies, including Internet of Things (IoT), artificial intelligence (AI), and advanced data analytics, into metal processing systems is explored. Case studies and simulations are conducted to assess the effectiveness of digitalization in enhancing processing efficiency, quality, and reliability. By leveraging digital technologies, opportunities for automation, predictive maintenance, and real-time monitoring are identified and evaluated.
Figure. 3 visualizes the impact of digital technologies, including Internet of Things (IoT), artificial intelligence (AI), and advanced data analytics, on various aspects of metal processing systems. Through simulations or case studies conducted over a 12-month period, the effectiveness of digitalization in enhancing processing efficiency, product quality, and reliability is assessed. The first subplot illustrates the trend of processing efficiency (%) over the 12-month period. Processing efficiency represents the effectiveness of the metal processing system in completing tasks or operations within a given timeframe. The blue line depicts the fluctuations in processing efficiency over time, indicating the impact of digital technologies on streamlining processes and optimizing resource utilization.
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Processing Efficiency Over "Time
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Product Quality Oyer Time
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Reliability Over Time
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Figure. 3 Impact of Digitalization on Metal Processing Systems
The second subplot in Figure. 3 displays the trend of product quality (%) over the same 12-month period. Product quality reflects the consistency and reliability of the final metal products produced by the processing system. The red line demonstrates the variations in product quality over time, showcasing the influence of digitalization on improving manufacturing processes and reducing defects. The third subplot showcases the trend of reliability (%) of the metal processing system throughout the 12-month duration. Reliability signifies the system's
ability to perform consistently and predictably over time, with minimal downtime or failures. The green line represents the changes in reliability over time, highlighting the enhancements achieved through the implementation of digital technologies, such as predictive maintenance and real-time monitoring.
By analyzing these trends, stakeholders can gain insights into the effectiveness of digitalization in enhancing processing efficiency, product quality, and reliability in metal processing systems. This information can inform decision-making processes and guide further investments in digital technologies to optimize metal processing operations.
Figure. 4 Integration of Reliability Theory in Metal Processing Operations
Reliability theory is integrated into the analysis to assess the probability of success and identify critical factors affecting reliability in metal processing operations. Quantitative reliability analysis techniques, such as failure mode and effect analysis (FMEA) and fault tree analysis (FTA), are utilized to evaluate the reliability of technological system structures and identify potential failure modes and their root causes. By addressing reliability concerns proactively, strategies for enhancing productivity and reliability in metal processing operations are developed.
Figure. 4 visualizes the integration of reliability theory into the analysis of metal processing operations, focusing on the probability of success and critical factors affecting reliability over a 12-month period. Through quantitative reliability analysis techniques such as failure mode and effect analysis (FMEA) and fault tree analysis (FTA), the reliability of technological system structures in metal processing is evaluated, and potential failure modes and their root causes are identified. The first subplot illustrates the trend of the probability of success over the 12-month period. The probability of success represents the likelihood of achieving desired outcomes or objectives in metal processing operations. The blue line depicts the variations in the probability of success over time, reflecting the effectiveness of strategies implemented to enhance reliability and productivity.
The second subplot displays the variations in critical factors affecting reliability over the same 12-month period. Critical factors represent the key variables or parameters that significantly influence the reliability of metal processing operations. Each line represents a specific critical factor, with changes in values over time indicating fluctuations in their impact on reliability. By analysing these trends, stakeholders can identify recurring patterns, prioritize areas for improvement, and develop proactive strategies to address reliability concerns effectively. This plot serves as a valuable tool for decision-makers and practitioners in the metal processing Qurilish va Ta 'lim ilmiy jurnali 3-jild, 3-son https://jurnal.qurilishtalim.uz
industry, enabling them to gain insights into the reliability of technological system structures, identify potential failure modes, and implement targeted interventions to enhance productivity and reliability in metal processing operations.
Results. The investigation into technological system structures in metal processing has yielded comprehensive insights across various domains. Papers explored the development of control and diagnostic systems for metal structures, the process approach to technology management in manufacturing companies, optimization problems in technological systems with parallel kinematic structures, and novel approaches to evaluating the complexity of technological systems in mining and metallurgical complexes. Collectively, these studies deepen our understanding of technological systems in metal processing, covering control methods, process management, optimization, and structural complexity evaluation, all of which offer valuable insights for enhancing efficiency and quality in metal processing operations. Metal processing industries, integral for economic development, face numerous challenges including machinery maintenance delays, low productivity, and environmental concerns. Strategies like Total Productive Maintenance, Systematic Layout Planning, Lean Manufacturing, and process standardization have shown promising results in improving productivity, reducing defects, and enhancing efficiency in metallurgical SMEs. Various methods such as forming, cutting, and coating are utilized to improve the physical and chemical performance of metal components. Additionally, advancements in metal processing not only drive productivity but also meet the requirements for diversified appearances and extended reliability testing times. Investigating technological system structures in metal processing remains crucial for enhancing productivity and reliability across the industry.
By integrating digital technologies like IoT, AI, and advanced data analytics, metal processing facilities stand to gain significant benefits. Digitalization offers opportunities for streamlining processes, improving efficiency, and ensuring quality through automation, predictive maintenance, and real-time monitoring. The integration of digital technologies into metal tube processing systems, for example, can enhance processing efficiency, quality, and facilitate seamless integration of steel and iron processing.
Discussion. The findings presented in this research paper shed light on the complexities and challenges faced by the metal processing industry, while also highlighting promising avenues for improvement and optimization. The discussion section aims to delve deeper into the implications of the results, offer insights into the broader context of the research, and suggest potential directions for future studies. The integration of digital technologies emerges as a pivotal factor in enhancing productivity, efficiency, and reliability in metal processing operations. By leveraging IoT, AI, and advanced data analytics, metal processing facilities can achieve unprecedented levels of automation and real-time monitoring, leading to improved decision-making processes and optimized resource utilization. The discussion could explore the scalability of digitalization efforts across different scales of metal processing facilities, considering factors such as cost-effectiveness, infrastructure requirements, and workforce training.
The incorporation of reliability theory into process improvement initiatives offers a proactive approach to mitigating risks and optimizing performance. By systematically assessing the probability of success and identifying critical factors affecting reliability, organizations can develop targeted interventions to address reliability concerns effectively. Discussion points may include the challenges associated with implementing reliability-focused strategies, such as data collection and analysis, organizational buy-in, and cultural shifts towards a proactive maintenance mindset. The metal processing industry faces numerous challenges, including machinery maintenance delays, low productivity, and environmental concerns. However, these challenges also present opportunities for innovation and improvement. Discussion could focus on strategies for overcoming specific challenges identified in the research findings, such as
implementing Total Productive Maintenance (TPM) to reduce machinery downtime or adopting Lean Manufacturing techniques to enhance productivity and quality. The research underscores the importance of adopting a holistic approach to technology management, encompassing technical, human, and organizational factors. By considering the interplay between these factors, organizations can develop sustainable solutions that drive continuous improvement and foster a culture of innovation. Discussion could explore the implications of this holistic approach for workforce development, organizational structure, and strategic decision-making processes within metal processing facilities. The findings presented in this research paper lay the groundwork for future studies in the field of metal processing. Future research could focus on exploring emerging technologies, such as additive manufacturing and advanced materials, and their impact on metal processing operations. Additionally, there is scope for further investigation into the integration of sustainability principles into metal processing practices, addressing environmental concerns and promoting sustainable development within the industry.
Conclusion. Through a review of various studies, we have elucidated the challenges faced by the metal processing industry, such as machinery maintenance delays, low productivity, and environmental concerns, while also exploring effective strategies for overcoming these challenges. The integration of digital technologies, including Internet of Things (IoT), artificial intelligence (AI), and advanced data analytics, emerges as a key driver of transformation in metal processing operations. Digitalization offers opportunities for streamlining processes, improving efficiency, and ensuring quality through automation, predictive maintenance, and real-time monitoring. By embracing digital technologies, metal processing facilities can optimize resource utilization, enhance decision-making processes, and drive continuous improvement in productivity and reliability. Moreover, the incorporation of reliability theory into process improvement initiatives represents a proactive approach to mitigating risks and optimizing performance. By systematically assessing the probability of success and identifying critical factors affecting reliability, organizations can develop targeted interventions to address reliability concerns effectively. This holistic approach to technology management underscores the importance of considering technical, human, and organizational factors in driving sustainable improvements in metal processing operations. The findings presented in this research paper not only contribute to advancing knowledge in the field of metal processing but also have practical implications for industry stakeholders. By leveraging the insights gained from this research, metal processing facilities can develop strategies to enhance productivity, efficiency, and reliability, thereby positioning themselves for success in a rapidly evolving market landscape.
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