Digitalization and Automation, Master of Science

Module 1: Consolidation of Mathematics and Technical Mechanics 

Students develop a deeper understanding of engineering concepts, enabling them to understand complex relationships, analyse theoretical and practical problems, and assess the quality of calculation results. Furthermore, the students are enabled to communicate and discuss complex issues in an understandable form within a team of employees. 

Module 2: Advanced Project Management 

Students explore agile methods and their application in manufacturing, preparing for digitalisation and business process restructuring. They learn to differentiate between classic and agile project management, define roles, plan complex projects, and integrate agile processes into company structures. Practical experience and teamwork skills are strengthened through group discussions and independent projects. 

Module 3: Big Data 

Student will dive into the topic of machine learning. They will focus on concepts, methods, and models such as cluster analysis, decision trees, and neural networks to address questions arising from large data sets. Students learn to evaluate and apply these techniques, understand their limitations, and gain skills in OLAP and data mining to assess business intelligence architectures and implement analysis processes. 

Module 4: Innovation and Digital Transformation 

This course teaches students how digital transformation drives market shifts and the need for companies to adapt and align business models with the digital developments. By the end, students can evaluate existing models and develop sustainable, complex digital strategies. 

Module 5: Legal and ethical issues in Engineering 

This module trains engineers to integrate ethical considerations when protecting ideas or technologies. Through case studies and research, students learn to identify and critically assess intellectual, social, and ethical issues in engineering, enhancing their ability to analyse challenges, coordinate discussions, and think critically. 

Module 6: Sustainable Development / Circular Economy 

This module emphasises the growing significance of sustainability in products and systems. Through lectures and best practice presentations, students connect theory with practice, learn to assess green engineering principles, and develop effective sustainability solutions. This training enhances their skills in tackling complex issues and fostering environmental responsibility. 

Module 7: Sensor Technology and Applications 

In this module, students learn to digitize analogue time signals, analyse them in the frequency domain, and implement digital adaptive controllers. They apply adaptive algorithms for system identification, noise suppression, and disturbance control. Additionally, students explore sensor technology, focusing on measurement systems used in quality control within manufacturing.

Module 8: Automation 

In this module, students gain expertise in programming real-time-capable Programmable Logic Controllers (PLCs). They learn to distinguish between microprocessors, microcontrollers, and digital signal processors, and analyse their functionality. The module also covers motor models, frequency control, drive unit design, and solutions for automation and functional safety issues.

Module 9: Advanced Materials and Technologies 

In this module, students learn about the latest 3D printing technologies. They explore how to decide which technology works best for different shapes and objects and learn how to choose the right method for each project. They master material characterization, quality issue resolution, and both additive and subtractive manufacturing methods. They also master slicing methods for product models and designing strategies for composite component production.

Module 10: Information Technology and Cyber Security 

Students learn the importance of data protection and security, including legal foundations, risks, and standards. They can design and implement IT security systems, improve communication in automation, and apply their knowledge to create and manage intelligent models for simulations in virtual and augmented reality.

Module 11: Digital Technologies 

The module provides in-depth knowledge of the Industrial Internet of Things (IIoT), simulation, and virtual reality, enabling students to tackle tasks at the intersection of engineering, data processing, and sustainability. Students learn to design IIoT components, use simulation and VR methods, and optimize models for industrial processes, improving human-machine interaction and technological integration.

Module 12: Management Accounting 

In this module, students learn how to plan, manage, and adapt business processes and strategies in response to changes in the environment, with a focus on maintaining financial stability and maximising profits. They also develop expertise in strategic and operational controlling, risk management, and making financial decisions under uncertainty, applying these skills through business management simulations. 

Module 13: Change Management and Leadership 

This module prepares students for leadership in global businesses by addressing strategic and operational challenges in a global enviornment. Through discussions and practical exercises, students build cultural awareness, explore diverse leadership styles, and learn to implement effective, sustainable strategies. 

Module 14: Quality Assurance and Quality Control 

Students learn to assess quality management systems and non-destructive testing methods, apply these procedures in small groups, and evaluate the results. They are trained to make responsible decisions on quality-relevant methods, critically analyse conventional and novel approaches, and develop improved solutions.

Module 15: Robotics 

In this module, students learn fundamental and advanced kinematic transformations for industrial robots. They master how to manage different types of robot motions, including complex movements and situations where the robot's movement options are limited. They gain expertise in KUKA Robot Language (KRL), robot motion planning, and programming, as well as designing digital robot twins with KUKA.Sim. Additionally, students learn to control dynamic systems with electric drives, develop robot controllers, and program Programmable Logic Controllers.

Module 16: Digital Image Processing 

Students learn to process and analyse digital images, understanding contrast, histograms, and lighting effects. They are also prepared for machine vision, integrating image processing with signal analysis and machine learning. At the conclusion of this module, students can, to the most extent, independently handle application-oriented projects, especially in the case of surveillance systems.

Module 17: Laboratory Automation 

In this module, students apply their knowledge in robotics, digital image processing, dynamic systems control, digital twin creation, and PLC programming to practical, industry-relevant tasks. They learn to identify, analyse, and solve complex technical problems, document and present experiments, and work constructively in diverse teams, enhancing their social and leadership skills.

Module 18: Project / Practice Module 

In this module, students will apply the knowledge gained throughout their studies to practical projects, thus qualifying them for the labour market. They will form interdisciplinary teams, working as independent members to tackle real-world problems. This will test their problem-solving skills, teamwork, project management skills, and ability to apply theory to practice. 

Module 18: Master´s Thesis
Master's Thesis and Disputation