Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Smart Manufacturing

    A tantárgy neve magyarul / Name of the subject in Hungarian: Intelligens gyártás

    Last updated: 2022. augusztus 29.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics     		Computer Engineering, BSc
    Course ID Semester Assessment Credit Tantárgyfélév
    VIETAD00   2/0/2/f 5  
    3. Course coordinator and department Dr. Illés Balázs György,
    4. Instructors

    Dr. Balázs Illés, full professor, ETT 

    Dr. Olivér Krammer, associate professor, ETT 

    Dr. Péter Martinek, associate professor, ETT 

    Dr. Attila Géczy, associate professor, ETT

    5. Required knowledge No different from the knowledge acquired during the first 3 semesters 
    6. Pre-requisites
    Ajánlott:
    -
    7. Objectives, learning outcomes and obtained knowledge
    The aim of the course is to familiarise students with the basics of intelligent manufacturing supported by information technologies, the concepts (e.g. Industry 4.0, IIoT) and the principles of the technologies involved. The objective of the course is to provide an overview of the trends in intelligent manufacturing, the sensor systems used to enable intelligent decision making, the principles of statistical data collection and evaluation and process control. The course will also introduce students to the basics of enterprise information systems, enterprise processes and enterprise management systems architecture. The course summarises the knowledge of manufacturing technology, mathematical-statistical and enterprise information technology that graduates will benefit from in order to acquire a basic understanding of the intelligent manufacturing of hardware and electronic components, to navigate the world of Industry 4.0 and to collaborate with industry specialists and researchers in this field.
    8. Synopsis
    Lectures:
     
    1. Objectives, topics and requirements of the course; introduction: an overview of the information technology-enabled manufacturing industry, the state of the domestic and international electronics industry. 

    2. Hardware prototyping, design tools; additive, 3D technologies, rapid prototyping, materials for additive manufacturing technologies, simpler additive manufacturing processes (e.g. photopolymerisation, fibre melt building); CAD systems, design workflows, 3D design, and related file formats. 

    3. Electronic device components: types of electronic components, printed circuit board (PCB) design, construction of multilayer printed wiring boards for general purpose and high frequency applications, electronic assembly technologies. 

    4. Applied sensing, sensor systems engineering, quantities to be measured/measurable, classification of sensors, typical application examples, location of sensors in manufacturing. 

    5. Data acquisition, sensor interfacing, digital buses, data acquisition devices, wired, wireless connections; a case study of temperature measurement from hardware and software side, in a manufacturing environment.
     
    6. The development of quality systems, ISO 9000 quality assurance, full quality systems, quality techniques, Quality 4.0 and future quality principles. 

    7. Statistical-mathematical foundations of quality, application of probability distributions in quality, parameters of variation, laws of large numbers, statistical software, and graphical representations of statistical data. 

    8. Basics of statistical sampling and sampling control, the AQL (Acceptable Quality Level) method, and its applications. Statistical sample estimation and estimation theory, the accuracy of sampling estimation, hypothesis testing, and correlation tests. 

    9. Fundamentals of statistical process control, process parameters and control charts, decision algorithms, machine and process performance indices and quality capacity. 

    10. Enterprise information systems; typical architecture modules, load sharing models, main supported enterprise processes.
     
    11. Production information systems, enterprise management systems, and their interrelationships, comprehensive models, and data models. Modeling of production processes, time management of production processes: available working time and time expenditure.

    12. Production, production models, production strategy, long-term production planning, medium-term production planning, product line planning, optimization of medium-term production planning, and computer support for planning.

     
    13. Production execution, characteristics, and algorithms of fine programming: scheduling for one and more machines.
     
    14. industry 4.0, hardware manufacturing technology, smart manufacturing - basics of machine learning methods related to hardware electronics manufacturing; machine-to-machine interfaces, extended human-machine communication tools for monitoring, maintenance, optimisation of machines on the shop floor; insights into the industry of the future. 
     
    Lab practices:
    1. 3D design processes, prototyping, 3D design systems, and simple practical example. 
    2. Implementation of selected prototype design in 3D design system, assigning homework. 
    3. Hardware manufacturing: interface assembly exercise, review of steps, application. 
    4 Applied sensing, sensor alignment (AD), sensor data acquisition. 
    5. Statistical analysis software, statistical analysis of manufacturing parameters and data 
    6. Visualisation of statistical data, and evaluation of results. 
    7. Supporting logistics processes in ERP system. 
    9. Method of instruction Lecture and lab pracrice
    10. Assessment 1 midterm exam and acceptance of the homework. The results of the summative assessment and the homework will be weighted 50-50% in the mid-term grading. 
    11. Recaps
    According to the TVSZ, there is a one possibility to supplement or improve the midterm exam. A 2nd supplementary midterm exam is only granted in case of low pass rates (less than two thirds) of previous exams. Submission of late homework by the end of the supplementary week.
    12. Consultations On a regular basis during the semester, by prior arrangement with the lecturers of the subject. 
    13. References, textbooks and resources

    Masoud Soroush, McKetta Michael Baldea, Thomas F. Edgar, Smart Manufacturing: Concepts and Methods 1st Edition, 2020 

     

    Illés Balázs, Krammer Olivér, Géczy Attila: Reflow Soldering: Apparatus and Heat Transfer Processes, Amsterdam, Hollandia, Elsevier (2020), 

     

    14. Required learning hours and assignment
    Kontakt óra56
    Félévközi készülés órákra42
    Felkészülés zárthelyire20
    Házi feladat elkészítése32
    Kijelölt írásos tananyag elsajátítása0
    Vizsgafelkészülés0
    Összesen150
    15. Syllabus prepared by

    Dr. Balázs Illés, full professor, ETT 

    Dr. Olivér Krammer, associate professor, ETT 

    Dr. Péter Martinek, associate professor, ETT 

    Dr. Attila Géczy, associate professor, ETT

    IMSc program In the laboratory practices, students participating in the iMSc program are placed in separate groups. For the students participating in the iMSc program, some laboratory practices will be supervised by the most experienced colleague in the field (who is/has been doing research in the field), who will introduce the students to the current research topics and recent results of the field in addition to the basic laboratory material.
    IMSc score IMSc scoring is based on the extra tasks given in the 1 midterm exam of the subject. The percentage of extra tasks in the midterm exam is 25%. Extra IMSc points can be obtained above a 75% pass mark in the midterm exam. The maximum IMSc score in the subject is 25. IMSc points are also available to students not participating in the iMSc program.