Knowledge Management in Cyber Physical Production Systems

Lecturer: Ansari Chaharsoughi, Fazel; Khobreh, Marjan

Syllabus outline:

• Basics of Cyber Physical Production Systems (CPPS) related concepts and terminologies including Digitalization, Industry 4.0, Digital Transformation in Manufacturing Enterprises, IoT, Digital Twin, etc.

• Basic of Knowledge Management

• Knowledge Management 4.0 and Big Data

• Artificial Intelligence and Cognitive Science  

• Human-Machine Interaction, Collaboration and Reciprocal Learning

• Collaborative Robotics

• Job Transformation in the Age of Industry 4.0

• CRISP-DM and Data Science Applications in Various Industrial Domains

• Knowledge-Based Maintenance (Maintenance wit/without sensing and computational technologies, Predictive and Prescriptive Maintenance, Text-Mining Applications in KBM, Industrial Use-cases)

• Knowledge Modeling and Representation with Ontology and Protégé Editor

• Human-Centered Cyber Physical Assembly Systems

• Excursion and Open Discussion at the TU Wien Pilot Factory Industry 4.0

Objectives and competences:

In this course, students develop a deep understanding of the topics described in the syllabus. In addition, students are enabled to use this newly gained knowledge in practice. More detailed information can be found in the sections “syllabus outline” and “intended learning outcomes”.

Intended learning outcomes:

After successful completion of the course, students are able to …

• … define and recognize CPPS and its associated data-driven processes in operational and management dimensions of a smart factory.

• Characterize and map “knowledge holders”, “knowledge generators”, “knowledge users” and “knowledge assets” in the context of CPPS, including human and technological entities such as AI agents and collaborative robots.

• Select appropriate methods and/or tools (platforms) of “Artificial Intelligence” and “Data Science”, including machine learning and predictive data analytics, knowledge engineering and semantic technology for modeling and analyzing data, learning new patterns, and reasoning (including prediction).

• Design the knowledge map of a smart factory (e.g. TU Wien Pilot-Factory) and select required data analytics and knowledge engineering methods not only for processing and utilizing CPPS data but also for supporting decision making processes