Course Topics
The course is composed of two modules whose topics are summarized as follows. Module 1 "Industrial Robotics": • introduction to robotics; • direct kinematics; • kinematic calibration; • inverse kinematics; • differential kinematics and statics; • trajectory planning; • basic components of robots; • robot dynamics and control (hints); • examples of programming of industrial robotic systems. Module 2 “Digital Manufacturing and Simulation”: Part 1) SIMULATION • Fundamentals of simulation modelling • Principles, methods and procedures for implementing simulation studies • Fields of application for simulation • Software tools for simulation Part 2) DIGITAL MANUFACTURING • Introduction to data-driven production • Industrial Internet of Things • Data Analytics and retrofitting of legacy systems • Work 4.0 and digital worker assistance systems • Digital twins in manufacturing • Manufacturing cybersecurity Simulation Lab: 1. Introduction to FlexSim 2. Data analysis and distributions 3. Case study modelling (production plant and/or logistics systems modelling and simulation) 4. Advanced features and Virtual Reality practice Industrial Internet of Things (IoT) Lab: 1. Introduction to the case study 2. Retrofitting of legacy equipment 3. Hardware set-up and software coding 4. Data extraction and analysis 5. KPI visualization on IoT-platform.

Teaching format
Frontal lectures. Practical parts and lab activities/exercises are planned also in the Smart Mini Factory laboratory.

Educational objectives
Knowledge and understanding: Module 1: The students will know the most important concepts of modelling, planning and control of industrial robotic systems and gain essential knowledge of robot components. Module 2: The student knows the basics and advanced features of simulation modelling and analysis as well as the current methods and tools for digitalization in manufacturing. Applying knowledge and understanding: The student applies and practices theoretical contents through exercises, case studies and project work. Theory contents are practiced through exercises using practical examples. From Module 1, the students will know how to treat a robotic system form a kinematic (position and speed) and static point of view as well as how to set-up a robotic simulator and motion control program. In Module 2 the students develop independently a simulation model for given case studies out from the production and logistics environment. In a second lab they practice IIoT and handling data with IoT platforms. Presentation techniques are trained using equipment such as flipcharts and power point presentations. Making judgements: Module 1: The student will be able to make judgments selecting a suitable robotic system for a practical industrial solution. Module 2: The student judges the use of appropriate methods, models and systems for simulation and Industrial IoT. Students can judge and interpret simulation results and data extracted from production and to use them for derivate measures for optimization. Communication skills: • Ability to present the acquired knowledge and competences with a proper language • Ability to express concepts with the field related technical terminology Learning skills: The students learn both by frontal teaching (theory part) as well as by exercises in the classroom and in the Smart Mini Factory lab (practical exercises). The students will be able to enlarge their knowledge through self-study and consultation of scientific and technical documentation.

Assessment
Assessment Module 1: Knowledge and understanding: written exam/reports Applying knowledge and understanding: individual/group work on assignment(s) Making judgements: individual/group work on assignment(s); Communication skills: group work on assignment(s), written/oral exam or presentation; Learning skills: individual/group work on assignment(s), written/oral exam or presentation. Assessment Module 2: Knowledge and understanding: written exam Applying knowledge and understanding: assignments in lab exercises Making judgements: assignments in lab exercises Communication skills: presentation of results of lab exercises Learning skills: lab exercises, written/oral exam Written exam means exam with review questions and exercises. Assignments in lab exercises means case study work and subsequent presentation of the results.

Evaluation criteria
Final single grade by arithmetic average of the grades obtained in Module 1 and Module 2, provided that sufficient grade is achieved in both modules. Module 1: The grade for this module is calculated as the arithmetic average of the written exam (50%) and the assignment (50%). Module 2: The grade for the module is calculated 50% from the results of the written exam and 50% from the results of the project work performed in the Simulation lab and IIoT lab. Criteria for the evaluation of the written examination: - completeness and correctness of the answers. Criteria for the evaluation of the assignment / project work / case study: - accuracy and completeness, as well as creativity in structuring of the proposed solution; - quality of the results and quality of presentation.