Smart Manufacturing und Factories
- Faculty
Faculty of Engineering and Computer Science
- Version
Version 1 of 23.03.2026.
- Module identifier
11B2348
- Module level
Bachelor
- Language of instruction
German
- ECTS credit points and grading
5.0
- Module frequency
only summer term
- Duration
1 semester
- Brief description
The theory behind the term 'Industry 4.0' requires a completely different way of thinking about the design and operation of manufacturing industries. It is based on the integration of existing technologies in order to create intelligent, networked and highly automated production environments that are capable of self-control. When implemented, this results in networked production systems - also known as smart factories. The development of corresponding production systems and areas requires an in-depth understanding of technological components, including those from areas unrelated to mechanical engineering, and calls for strong interdisciplinary thinking. Furthermore, knowledge and in-depth understanding of process-related recording and internal factory processing of process and product data is required, taking into account economic and ecological sustainability.
Excursions are carried out as required to accompany the course.
- Teaching and learning outcomes
Part A:
1. basics -> value chain, industrial revolutions, past and present technology drivers, manufacturing processes in the context of efficiency - ecology - sustainability2. digital twins -> product/process/resource modelling, communication between reality and virtuality, real-time synchronization, simulation models, virtual commissioning
3. networking of machine tools -> holistic and adaptive manufacturing systems, integration of autonomous sensor-based systems, information technology in the machine tool environment, machine-to-machine communication
Block B:
4. technologies in networked production -> Internet of Things, cyber-physical systems, cloud computing, service-oriented architectures, standardized interfaces and protocols, industrial bus systems, real-time capability5. data acquisition and analysis -> data in the context of product - process - resource, production data acquisition, data storage, analysis and logging functions
6. self-control of production and assembly systems -> coordination of the value chain, use of autonomous machines and robots, adaptable production units, optimization with regard to raw material efficiency and CO2 footprint
- Overall workload
The total workload for the module is 150 hours (see also "ECTS credit points and grading").
- Teaching and learning methods
Lecturer based learning Workload hours Type of teaching Media implementation Concretization 45 Lecture Presence - 15 Laboratory activity - Lecturer independent learning Workload hours Type of teaching Media implementation Concretization 60 Preparation/follow-up for course work - 30 Exam preparation -
- Graded examination
- Portfolio exam
- Ungraded exam
- Field work / Experimental work
- Remark on the assessment methods
Graded examination performance:
The portfolio examination performance consists of a written project report (PSC) and a term paper (HA) completed during the semester. The total number of points is 100, of which a maximum of 50 points can be achieved in the written project report and in the term paper.
- Exam duration and scope
Graded examination: Portfolio examination:
- Written project report (as part of a portfolio examination): 10–12 pages
- Term paper (as part of a portfolio examination): 10–12 pages
Ungraded examination:
- Experimental work: approx. 3 to 5 experimental tasks
- Knowledge Broadening
Students have a holistic view of digitalization in the production environment and can differentiate between the main existing technologies. After completing the module, students will be able to differentiate between the various technologies of networked production and assign them to possible manufacturing processes. They distinguish between reality and virtuality and explain the benefits of intensive data collection and use against the background of economic efficiency and sustainab
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- Knowledge deepening
After completing the module, students will be able to differentiate between the necessary data streams within a digitalized production process and integrate standards with regard to data acquisition and data storage. They explain the use of adaptive manufacturing systems with a holistic view of production areas and illustrate the benefits within the production-related value chains.
- Literature
Bauernhansl, Thomas; ten Hompel, Michael; Vogel-Heuser, Birgit: Industrie 4.0 in Produktion Automatisierung und Logistik; Springer Vieweg Wiesbaden; 2014
Bauernhansl, Thomas; ten Hompel, Michael; Vogel-Heuser, Birgit: Handbuch Industrie 4.0 Bd. 1-4; Springer Vieweg Berlin; 2017
Kletti, Jürgen; Rieger, Jürgen: Die perfekte Produktion - Manufacturing Excellence in der Smart Factory; 3. Auflage; Springer Vieweg Berlin; 2023
Langmann, Reinhard: Vernetzte Systeme für die Automatisierung 4.0; Hanser München; 2021
Czichos, Horst: Cyber-physische Systeme und Industrie 4.0; Springer International Publishing Cham; 2024
Steven, Marion: Smart Factory - Einsatzfaktoren - Technologie - Produkte; Kohlhammer Stuttgart; 2020
Seitz, Matthias: Speicherprogrammierbare Steuerungen für die Fabrik- und Prozessautomation; Hanser München; 2015
Seitz, Matthias: Speicherprogrammierbare Steuerungen in der Industrie 4.0: Objektorientierter System- und Programmentwurf, Motion Control, Sicherheit, Industrial IoT; 5. Auflage; Hanser München; 2021
Reinhart, Gunther: Handbuch Industrie 4.0, Carl Hanser Verlag; 2017
Vogel-Heuser, Birgit; Bauernhansl, Thomas; Hompel, Michael: Handbuch Industrie 4.0 Band 2, Springer Vieweg, 2. Auflage, 2017
Westk?mper, Engelbert; L?ffler Carina: Strategien der Produktion, Springer Vieweg, 2016
- Applicability in study programs
- Mechanical Engineering (Bachelor)
- Mechanical Engineering B.Sc. (01.09.2025)
- Mechanical Engineering in Practical Networks
- Mechanical Engineering in Practical Networks B.Sc. (01.03.2026)
- Mechatronics
- Mechatronics B.Sc. (01.09.2025)
- Automotive Engineering (Bachelor)
- Automotive Engineering B.Sc. (01.09.2025)
- Person responsible for the module
- Sachnik, Peter
- Teachers
- Sachnik, Peter