Intelligent Systems and Machine Learning for Production Processes

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After attending the module, students are able to approach the development of complex intelligent mechatronic systems in a structured way. In addition to methodical competence, they also have an overview of relevant technologies for the implementation of such intelligent systems.

The goal of the module ISMLP is to convey a structured development methodology for intelligent mechatronic systems. In addition to the special features of technical systems and their development, this course teaches methods from research and industry that enable students to develop structured approaches. For this purpose, different description tools are taught, which can be used to model mechatronic systems. This includes the Systems Modeling Language (SysML), which is established in Model-Based Systems Engineering, as well as, for example, the Object Constraint Language (OCL), which is widely used in computer science and enables an automated testing of the models created. The modeling is supplemented by the aspect of intelligence of such technical systems. For this purpose, concepts of agent-based systems are taught, which enable self-organisation of mechatronic systems and thus increase their flexibility and adaptability. In addition, Semantic Web Technologies as a technology for knowledge representation as well as approaches of data analysis and machine learning in the context of mechatronic systems are central parts of the lecture. The lecture "Intelligent Systems and Machine Learning for Production Processes" complements and replaces the lecture "Entwicklung intelligenter verteilter eingebetteter Systeme in der Mechatronik".

No previous knowledge is required for the lecture.

The course will be digitized in SS20 (educational videos and online consultations)

Written Exam (90 Minutes)

Göhner, Peter: Agentensysteme in der Automatisierungstechnik. Xpert.press, 2013. Paulo Leitão, Stamatis Karnouskos: Industrial Agents - Emerging Applications of Software Agents in Industry. Elsevier, 2015. Friedenthal, Sanford; Moore, Alan; Steiner, Rick: A Practical Guide to SysML. MK/OMG Press, 2015. Gopinath Rebala, Ajay Ravi, Sanjay Churiwala: An Introduction to Machine Learning. 2019.

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In addition to the lecture of the same name, the exercise offers the possibility to practice the methods taught by means of selected examples. In addition to the modeling of systems using SysML and OCL, the students are given the opportunity to apply methods of machine learning and knowledge representation. This is complemented by the joint analysis of scientific publications. Together with the lecture of the same name, this exercise provides students with methodological competence and an overview of relevant technologies for the implementation of intelligent mechatronic systems.

Die Lehrveranstaltung wird im SS20 digital (Lehrvideos und online Sprechstunden) durchgeführt