Детальная информация

Настоящая монография ориентирована на решение комплекса научно-практических задач, связанных с повышением эффективности управления Big Data на машиностроительных предприятиях. В рамках исследования рассматриваются вопросы снижения трудоёмкости этапов технологической подготовки производства за счёт комплексной автоматизации процессов проектирования, сокращения времени сбора, обработки и анализа данных в производственном цикле изготовления изделий, а также уменьшения себестоимости продукции на основе системного анализа альтернативных вариантов организации производственных потоков, технологических маршрутов и использования ресурсов. Монография подготовлена в рамках реализации проекта грантового финансирования проектов Комитета по науке Министерства науки и высшего образования Республики Казахстан (Грант ИРН № AP23490796 «Разработка цифровых моделей производственного процесса, основанных на многокритериальном анализе управления Big Data в рамках технологической подготовки производства машиностроительных предприятий»).

This monograph addresses a range of scientific and practical challenges related to improving the efficiency of Big Data management at mechanical engineering enterprises. The study examines reducing the labor intensity of pre-production stages through comprehensive automation of design processes, reducing the time it takes to collect, process, and analyze data during the manufacturing cycle, and reducing production costs through a systems analysis of alternative production flow, process routes, and resource utilization options. The monograph is prepared within the framework of realization of the project of grant financing of projects of the Committee on Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant IRN AP23490796 “Development of digital models of the production process based on multi-criteria analysis of BigData management within the framework of technological preparation of production of machine-building enterprises”).

• Содержание
• INTRODUCTION
• 1 ANALYSIS OF THE STRUCTURE AND PARAMETERIZATION OF THE TECHNOLOGICAL PROCESS
• 1.1 Stages of the technological process
• 1.2 Operation parameters
• 1.3 Input and output parameters for Big Data
• 2 COLLECTION AND PROCESSING OF PRIMARY INFORMATION (BIG DATA)
• 2.1 Sources of process data in digital manufacturing
• 2.2 Cleaning, normalization, and aggregation of process data
• 2.3 Formation of features for multicriteria analysis
• 3 ANALYSIS OF MULTIPARAMETER OPTIMIZATION METHODS
• 3.1 Optimization criteria
• 3.2 Methods of multiparameter optimization of technological processes
• 3.3 Technological limitations
• 4 MODEL FOR OPTIMIZATION OF FORMING PARAMETERS
• 4.1 Structural hierarchical model of the process of forming a design element
• 4.2 Set-theoretical models of structural elements of the surface formation process
• 4.3 Statement of the problem of controlling the process of forming a design element
• 4.4 Optimization of the process of forming a group of threaded holes
• 5 INTELLIGENT METHODS OF ANALYSIS, FORECASTING AND CONTROL OF THE PRODUCTION PROCESS
• 5.1 Analysis of production data and performance indicators (CT, WIP, TH, OPEX)
• 5.2 Intelligent quality control and defect prediction
• 5.3 Parameterization and modeling of the structure of material flows
• 5.4 Multicriteria optimization and intelligent control methods
• 6 METHODOLOGY FOR OPTIMIZING MATERIAL FLOWS
• 6.1 Synchronization of operations
• 6.2 Workpiece and tool flow management
• 6.3 Practical testing of a digital system for optimizing mechanical processing processes
• 6.4 Integration into the Chamber of Commerce and Industry and MES/ERP
• CONCLUSION
• REFERENCES