Course content
Students
can
- understand and discuss applications, potentials and implementation hurdles of industry 4.0 or cyber-physical production systems for sustainable production
- can name and evaluate current and future technologies of digitization and select them as solution modules for the design of cyber-physical production systems
- can explain the essential modeling approaches of data analysis and simulation and can describe their basic modeling principles, application possibilities and general conditions
- can name and discuss the phases and essential methods of data analysis according to Knowledge Discovery in Databases (KDD) and the Cross-Industry Standard Process for Data Mining (CRISP-DM)
- are able to apply individual modelling approaches on the basis of simple use cases in production
- are able to use software tools for data analysis, using their own gathered production data, in order to make decisions on production control
- can, based on the laboratory, apply and deepen the learned methods on a real machine tool or develop e.g. mixed reality applications
- are able to effectively organize themselves in a group work, divide the work, ensure that goals are achieved on time and practice solution-oriented communication
Content:
- (Sub-)Elements of cyber physical production systems
- Trends and technologies for data acquisition and treatment
- Trends and technologies for decision support and automated control in manufacturing
- Standardized processes for data analysis (CRISP-DM, KDD)
- Data-based modeling (unsupervised and supervised machine learning methods)
- Simulation approaches (e.g. discrete-event simulation, agent-based simulation)
- Application areas and examples on different factory scales (production processes and chains, technical building services, factory shell)
- Target conflicts of cyber physical productions systems in the context of sustainable manufacturing
- Practical application of data mining methods and tools in the context of the IWF learning factory
Laboratory Data Mining in Production: In-depth practical aspects of the lecture with focus on data mining methods and tools. A real machine tool is used as a use case and data acquired during manufacturing is evaluated.
Labor Mobile Applications for Sustainable Manufacturing: In-depth practical aspects in the development of mobile software applications in manufacturing environments
