A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Intelligens gyártás
Smart Manufacturing
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVIETAD00 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
DR. Illés Balázs György
position: egyetemi tanár
contact:
illes.balazs@vik.bme.hu
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| Responsible department |
Elektronikai Technológia Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
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.
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.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
Lecture and lab pracrice
Tanulástámogató anyagok
Online források
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),
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
No different from the knowledge acquired during the first 3 semesters
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
No different from the knowledge acquired during the first 3 semesters
General rules
Requirements:
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.
Additional possibilities:
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.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
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 points:
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.
Recommended courses
-
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.