Module Handbook Master of Science in Robotic …...1 Table of Contents Compulsory Courses – First Term.....3 Module: Robotic Systems 3 Module: Advanced Robotic Kinematics and Dynamics

Module Handbook

‘Master of Science in Robotic Systems Engineering’ (M.Sc.)

1

Table of Contents

Compulsory Courses – First Term ......................................................................................... 3

Module: Robotic Systems ................................................................................................... 3

Module: Advanced Robotic Kinematics and Dynamics ....................................................... 7

Module: Control Engineering ............................................................................................ 11

Module: Electrical Drives .................................................................................................. 14

Module: Machine Learning ............................................................................................... 17

Module: Computer Science in Mechanical Engineering II ................................................. 21

Module: German Language Course ................................................................................. 24

Compulsory Courses – Second Term .................................................................................. 27

Module: Multibody Dynamics ............................................................................................ 27

Module: Computer Vision I ............................................................................................... 31

Module: Robotic Sensor Systems ..................................................................................... 35

Elective Courses – Second Term ......................................................................................... 38

Module: Computer Vision II .............................................................................................. 38

Module: Production Metrology .......................................................................................... 41

Module: Machine Dynamics of Rigid Systems .................................................................. 45

Module: Industrial Logistics .............................................................................................. 49

Module: Artificial Intelligence and Data Analytics for Engineers ........................................ 52

Module: Factory Planning ................................................................................................. 55

Module: Summer School – Advanced Topics in Robotic Systems .................................... 59

Engineering ...................................................................................................................... 59

Compulsory Course – Third Term........................................................................................ 61

Module: Simulation of Robotic Systems, Sensors and Environment ................................. 61

Elective Courses – Third Term ............................................................................................. 65

Module: Introduction to Artificial Intelligence ..................................................................... 65

Module: Advanced Machine Learning............................................................................... 68

Module: Power Electronics ............................................................................................... 71

2

Module: Processes and Principles of Lightweight Design ................................................. 74

Module: Applied Numerical Optimization Engineering ...................................................... 77

Module: Numerical Methods in Mechanical Engineering .................................................. 81

Module: Strategic Technology Management..................................................................... 85

Module: Finite Element Methods for Engineers ................................................................ 89

Module: Mechatronics and Control Techniques for Production Plants .............................. 93

Module: Advanced Control System .................................................................................. 97

Elective Courses – Fourth Term ........................................................................................ 100

Module: Internship (Industrial Track) .............................................................................. 100

Module: Research Project (Academic Track) .................................................................. 102

Compulsory Course – Fouth Term ..................................................................................... 104

Module: Master Thesis ................................................................................................... 104

3

Compulsory Courses – First Term

Module: Robotic Systems

Module Robotic Systems

Module level Master

Subtitle RS

Lecture See list of lectures and examinations of the module

Semester 1

Person in charge apl. Prof. Dr.-Ing. Mathias Hüsing

Lecturer apl. Prof. Dr.-Ing. Mathias Hüsing

Language English

Assignment to the curriculum

Compulsory Module

Teaching form Examination , Lecture , Exercise

Workload Total 150hmin, Lecture hours 60h, Self-study 90h

Lecture hours 60h

ECTS-Credit Points (CP)

5

Requirements according to examination regulation

-none-

Learning objectives

Robotic Systems

Overall goal: The students have a profound comprehension of the fundamentals of robotic systems as well as the components used to build and run a robotic system. Thus, they are capable of comprehending, describing and analyzing robotic systems and components. After successfully completing this course, the students will have acquired the following learning outcomes:

Knowledge / Understanding

Students

have a profound comprehension of the fundamentals of robotic

systems as well as the components used to build and run a robotic

4

system. Thus, they are capable of comprehending, describing and

analyzing robotic systems and components.

Abilities / Skills / Competencies:

Students

got a brief overview about existing and future robotic systems. The

students are capable of running through the development and

implementation process of a mechatronic robotic gripper;

analyse the kinematic structure of robots as well as grippers.

Furthermore, they have the knowledge and the ability to launch and

use general robotic components (stepper motor, sensors) and control

(via microcontroller) the kinematic structures to complete it to a full

mechatronic system;

use general methods of structural synthesis and follow the

development guidance for mechatronic systems (VDI 2206).

Content

Robotic Systems

1st Lecture Introduction to Industrial Robots (History of Robotics, Definition of Robotics, World Robotic Market, Requirements and application scenario, Essential construction elements of an industry robot, Category of robotics, Robotic Companies and StartUps, Future smart and intelligent Robots)

2nd Lecture Introduction to Advanced Robots (Advanced, Space, Food, Medical, Home Cleaning Robots, Mobile Manipulators, Intelligent Vehicles, World Robotic market: Service Robotics)

3rd Lecture General Robot Structures (Joints and Motion, Degree of Freedom, Workspaces, Different Classifications)

4th Lecture Structural Synthesis (Selection of robotic structures / quantitative optimization)

5th Lecture Robot End-effector Technology (Types and function of different End-effector technologies)

6th Lecture Gripper Technology (Characteristics of Objects, The Grasp, Gripper Mechanisms, Merit Indices, Design)

7th Lecture Components of Robotic Systems (Gears)

8th Lecture Components of Robotic Systems (Actuators)

9th Lecture Components of Robotic Systems (Sensors and Vision Systems)

10th Lecture Components of Robotic Systems (Control and Safety Architecture)

11th Lecture Properties and Benchmarking (Performance evaluation)

12th Lecture Mobile Manipulators (Types of Wheels, Kinematic Constrains, Robot Configuration Variables, Characterization of robot mobility, Wheeled Robot Structures)

13th Lecture Control and Path Planning (Artificial Intelligence)

Media Lecture and Exercise slides

5

Literature

- Siciliano, B.: Robotics; Modelling, Planning and Control, Springer International Publishing, 2009, eBook ISBN 978-1-84628-642-1, DOI 10.1007/978-1-84628-642-1

- Siciliano, B. (Hrsg.): Springer Handbook of Robotics, Springer International Publishing, 2016, eBook ISBN 978-3-319-32552-1, DOI 10.1007/978-3-319-32552-1

Lectures / Examinations

Title Code ECTS

Workload (SWS / h) Duration of Exam (min)

Lecture h. (SWS)

Self-Study (h)

Examination: Robotic Systems 5 0 0

See examination options

Lecture: Robotic Systems

0 2 45 0

Exercise: Robotic Systems

0 2 45 0

Teaching Unit / Examinations: Examination Robotic Systems

Title Examination Robotic Systems

Sub-title Exa RS

Semester 1

Connection to the curriculum

Compulsory Module

Teaching Unit / Examinations: Lecture Robotic Systems

Title Lecture Robotic Systems

Sub-title L RS

Semester 1


Compulsory Module

Teaching Unit / Examinations: Exercise Robotic Systems

Title Exercise Robotic Systems

Sub-title E RS

Semester 1

6


Compulsory Module

7

Module: Advanced Robotic Kinematics and Dynamics

Module Advanced Robotic Kinematics and Dynamics

Module level Master

Subtitle ARKaD


Semester 1

Person in charge Univ.-Prof. Dr.-Ing. Dr. h. c. (UPT) Burkhard Corves

Lecturer Univ.-Prof. Dr.-Ing. Dr. h. c. (UPT) Burkhard Corves

Language English


Compulsory Module

Teaching form Examination, Lecture, Exercise

Workload Total 150h, Lecture hours 60h, Self-study 90h

Lecture hours 60h


3


-none-

Learning objectives

Advanced Robotic Kinematics and Dynamics Overall goal: The students have a profound comprehension of the fundamentals of robotic kinematics and dynamics. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

have a profound comprehension of the fundamentals of robotic

kinematics and dynamics;

know the position, orientation and rotation matrix + homogeneous

transformations and coordinate systems;

recognise direct and inverse kinematics;

know how to use differential and inverse differential kinematics and

statics;

know the dynamic model calculations.

8


Students

set up the algorithms that are necessary to calculate position,

velocities and accelerations of robotic systems and have a

comprehensive understanding of the mathematical descriptions of the

movement states;

deploy and use the DH-notation for robotic systems. At the same time,

they consider the requirements of engineering science for different

robotic structures;

select suitable robotic structures for the relevant handling tasks, to

recognise important parameters and describe them mathematically

correct to implement them into a programming;

program a robotic trajectory in joint and cartesian space and execute it

in simulations.

Content

Advanced Robotic Kinematics and Dynamics

1st Lecture Introduction of Robotic Systems (Industrial root brief introduction, Modelling, Planning and Control)

2nd Lecture Position, Orientation and Rotation Matrix (Pose of Rigid Body, Rotation Matrix, Composition of Rotation Matrices, Euler Angles, Axis and Angle, Unit Quaternion)

3rd Lecture Coordinate System/Homogeneous Transformations/Joints (Coordinate Systems, Homogeneous transformations, Joints)

4th Lecture Direct Kinematics – Serial/Parallel (Direct Kinematics --> Two planar arm, Denavit-Hartenberg Convention, Kinematics of typical manipulator structures)

5th Lecture Inverse Kinematics (Joint and operational space, workspace, redundancy, Inverse kinematics, Problems and Properties, Analytical and Numerical Solutions)

6th Lecture Differential Kinematics (Definition, geometric Jacobian, Jacobian for typical manipulator Structures, Kinematic singularities)

7th Lecture Inverse Differential Kinematics and Statics (Definition, Calculation methods, Jacobian transpose and statics, velocity and force)

8th Lecture Modelling of Dynamics Model (Direct and Inverse Dynamics definition, Mechanics, Modelling of a rotary drive system, Lagrange Formulation, Examples)

9th Lecture Notable Properties of Dynamic Model (Analysis, Properties, Extensions, Parametrization, identification, uses)

10th Lecture Newton-Euler Formulation (Derivative of a vector in moving frame, Dynamics of a rigid body, recursive algorithm)

11th Lecture Trajectory Planning in Joint Space (Path and Trajectory, Point-to-Point motion, Motion through a sequence of points)

12th Lecture Trajectory Planning and Optimization in Cartesian Space (Path Primitives. Position and Orientation Planning, Optimal Trajectory Planning)

13th Lecture Kinematic Control (Definition of robot motion control and kinematic control, joint and Cartesian space control)

9

14th Lecture Dynamic Control (Dynamic Model and its control properties, P/PD/PID control law)

Media - Lecture slides

- Exercise slides

Literature

- Siciliano, B.: Robotics; Modelling, Planning and Control, Springer International Publishing, 2009, eBook ISBN 978-1-84628-642-1, DOI 10.1007/978-1-84628-642-1

- Siciliano, B. (Ed.): Springer Handbook of Robotics, Springer International Publishing, 2016, eBook ISBN 978-3-319-32552-1, DOI 10.1007/978-3-319-32552-1


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Advanced Robotic Kinematics and Dynamics

5 0 0 120

Lecture: Advanced Robotic Kinematics and Dynamics

0 2 45 0

Exercise: Advanced Robotic Kinematics and Dynamics

0 2 45 0

Teaching Unit / Examinations: Examination Advanced Robotic Kinematics and Dynamics

Title Examination Advanced Robotic Kinematics and Dynamics

Sub-title Exa ARKaD

Semester 1


Compulsory Module

Teaching Unit / Examinations: Lecture Advanced Robotic Kinematics and Dynamics

Title Lecture Advanced Robotic Kinematics and Dynamics

Sub-title L ARKaD

Semester 1


Compulsory Module

10

Teaching Unit / Examinations: Exercise Advanced Robotic Kinematics and Dynamics

Title Exercise Advanced Robotic Kinematics and Dynamics

Sub-title E ARKaD

Semester 1


Compulsory Module

11

Module: Control Engineering

Module Control Engineering

Module level Master

Subtitle CE


Semester 1

Person in charge Univ.-Prof. Dr.-Ing. Dirk Abel

Lecturer Dipl.-Ing. Uwe Jassmann

Language English


Compulsory Module



Lecture hours 30h


3


Basic knowledge in mathematics as defined in the examination regulations.

Learning objectives

Control Engineering

After successfully completing this course, the student will have acquired the following learning outcomes:

Knowledge / Understanding:

Students

know, recognize and classify the most common linear control loop

elements;

understand the effects of feedback and apply different methods to set

up feedback elements (controllers) such that predefined control goals

are met.

Abilities / Skills:

Students

analyze dynamical, biological and biomedical systems and identify the

relevant causalities;

employ different mathematical descriptions of dynamical systems;

12

solve differential equations by means of Laplace transform;

assess of the stability of dynamical systems using different methods;

obtain, interpret and employ the frequency response of dynamical

systems.

Competencies

Students

show analytical thinking with respect to causality of dynamics system in real-world applications.

Content

Control Engineering

Functional diagrams

Linearization

Set up and solving of differential equations

Features in time domain of dynamical systems

Laplace transform and transfer function

Functional diagram algebra

Frequency response

Bode diagram and Nyquist plot

Linear control loop elements

Principle and goals of controller design

Steady state analysis and transient performance of a control loop

Controller setting rules

Stability of control loops: Nyquist stability criterion, phase margin, gain margin, controller design in bode diagram, algebraic stability criteria,

Media e-Learning L2P, Power Point

Literature Lecture Notes

Students also receive a list of relevant literature


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Control Engineering

3 0 0

Max. 60 (oral) or 120 (written)

Lecture: Control Engineering

0 1 30 0

13

Exercise: Control Engineering

0 1 30 0

Teaching Unit / Examinations: Examination Control Engineering

Title Examination Control Engineering

Sub-title Exa CE

Semester 1


Compulsory Module

Teaching Unit / Examinations: Lecture Control Engineering

Title Lecture Control Engineering

Sub-title L CE

Semester 1


Compulsory Module

Teaching Unit / Examinations: Exercise Control Engineering

Title Exercise Control Engineering

Sub-title E CE

Semester 1


Compulsory Module

14

Module: Electrical Drives

Module Electrical Drives

Module level Master

Subtitle ED


Semester 1

Person in charge Univ.-Prof. Dr. ir. Dr. h. c. (RTU) Rik W. De Doncker

Lecturer Lecture: Univ.-Prof. Dr. ir. Dr. h. c. (RTU) Rik W. De Doncker

Exercise: Research Associates

Language English


Compulsory Module



Lecture hours 45h


4


none

Learning objectives

Electrical Drives

Overall goal: Understanding, modelling and control of electrical drives based on the most common electrical machine types. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

know the working principals of the most common electrical machine

types;

know and understand the modelling of modern drive systems.

Abilities / Skills

Students

15

distinguish between dynamic control strategies such as field-oriented

and direct-torque control and their sensible applications;

recall the requirements of the different machines concerning sensors

and power electronics.

Competencies

Students

choose electrical machines and converter topologies based on

application requirements;

design electric drive trains and their control;

present complex relationships and are able to explain them to

experts as well as to a non-expert group of people.

Content

Electrical Drives

Electrical drives are used in many different fields: at home, in industry and for transportation. Dental drills as well as hybrid or fully electric vehicles and ships are powered by electrical motors. The advantages of electrical drives are that electricity is applicable almost everywhere and comparatively easy to decentralize, power and velocity are easy to control, the maximum machine torque is available at zero speed and wear and maintenance costs are low. Particularly due to their high efficiency, electrical drives score well. Since electrical drives consume about 60% of all electrical energy used in industry and gain more and more importance in the field of personal mobility, a huge amount of energy can be saved by an intelligent control of electrical motors. The above mentioned control of electrical motors is the topic of the lecture Electrical Drives. Subsequent to a short introduction to the mechanics of rotating systems the control of all common electrical machines (dc, synchronous, induction and switched reluctance machine) is presented. The universal field oriented (UFO) concept is explained which demonstrates the concepts of modern vector control and exemplifies the seamless transition between so called stator flux and rotor flux oriented control techniques. This powerful tool is used for the development of flux oriented machine models of rotating field machines. These models form the basis of UFO vector control techniques which are covered extensively together with traditional drive concepts. Attention is also given to the dynamic modeling of Switched Reluctance (SR) drives, where a comprehensive set of modeling tools and control techniques is presented. The lecture should appeal to students who have a desire to understand the intricacies of modern electrical drives without losing sight of the fundamental principles. It brings together the concepts of the ideal rotating transformer (IRTF) and UFO which allows a comprehensive and insightful analysis of ac electrical drives in terms of modeling and control. Extensive use is made of build and play modules which provide the student with the ability to interactively examine and understand the presented topics.

Media Presentation slides, scripts, exercises

Literature De Doncker, Pulle, Veltman: Advanced Electrical Drives


Title Code ECTS Workload (SWS / h)

16

Lecture h. (SWS)

Self-Study (h) Duration of Exam (min)

Examination: Electrical Drives

4 0 0 90

Lecture: Electrical Drives

0 2 30 0

Exercise: Electrical Drives

0 1 45 0

Teaching Unit / Examinations: Examination Electrical Drives

Title Examination Electrical Drives

Sub-title Exa ED

Semester 1


Compulsory Module

Teaching Unit / Examinations: Lecture Electrical Drives

Title Lecture Electrical Drives

Sub-title L ED

Semester 1


Compulsory Module

Teaching Unit / Examinations: Exercise Electrical Drives

Title Exercise Electrical Drives

Sub-title E ED

Semester 1


Compulsory Module

17

Module: Machine Learning

Module Machine Learning

Module level Master

Subtitle ML


Semester 1

Person in charge Univ.-Prof. Dr. sc. techn. Bastian Leibe

Lecturer Univ.-Prof. Dr. sc. techn. Bastian Leibe

Language English


Compulsory Module



Lecture hours 60h


6


It is advised to have knowledge in Linear algebra and probability theory and statistics

Learning objectives

Machine Learning

On successful completion of this module, students should be able to recall and explain the theoretical foundations and concepts underlying Machine Learning techniques, in particular:


Students have a profound knowledge in;

Bayes decision theory

Probability density estimation: non-parametric vs. parametric methods

Maximum Likelihood vs. Bayesian estimation

Linear classifiers, least-squares classification, generalized linear classifiers, Fisher linear discriminant analysis, logistic regression

Empirical/structural Risk minimization, VC dimension

Support Vector Machine

Ensemble methods, Boosting, AdaBoost

Decision trees: attribute selection, Random Forests, extremely randomized trees, ferns

18

Probabilistic Graphical Models: Bayesian Networks, Markov Random Fields, and Factor Graphs; factorization; conditional independence;

Exact inference: belief propagation; junction tree algorithm; graph cuts algorithm.

Abilities / Skills

Students

derive, explain and apply the following practical machine learning methods and algorithms:

Probability density estimation: Maximum likelihood, Kernel/k-Nearest Neighbor density estimation, k-Means, EM algorithm for mixture-of-Gaussians estimations

Linear classifiers: Least-squares classification

Support Vector Machines

AdaBoost

Decision Trees

Random Forests

Sum-Product Belief Propagation

Junction Tree algorithm

Graph Cuts algorithm

Competencies Students

discuss the advantages and disadvantages of the covered machine

learning techniques;

find practical solutions to complex real-world machine learning

problems;

work on practical problems in a team.

Content

Machine Learning

The goal of Machine Learning is to develop techniques that enable a machine to "learn" how to perform certain tasks from experience. The important part here is the learning from experience. That is, we do not try to encode the knowledge ourselves, but the machine should learn it itself from training data. The tools for this are statistical learning and probabilistic inference techniques. Such techniques are used in many real-world applications. This lecture teaches the fundamental machine learning techniques that underlie such capabilities. In addition, it shows current research developments and how they are applied to solve real-world tasks. The detailed lecture topics include:

Basic concepts: Introduction to probability theory, Bayes decision theory

Probability Density Estimation: Parametric methods, maximum likelihood, mixture models, EM, nonparametric methods, histograms, k-NN, kernel density estimation

Discriminative Methods for Classification: Linear discriminants, statistical learning theory, support vector machines, model combination & ensemble methods, bagging, boosting, AdaBoost, decision trees, randomized trees, random forests and ferns, model selection

19

Probabilistic Graphical Models: Bayesian networks, Markov random fields, factor graphs, conditional independence, exact inference: message passing, Belief Propagation, junction tree algorithm, graph cuts

Media Lecture script in form of printed slides, Additional handouts for certain topics, Web page with supplementary material and exercises: http://www.mmp.rwth-aachen.de/teaching

Literature

C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006

R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd Edition, Wiley-Interscience, 2000


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Machine Learning 6 0 0 90

Lecture: Machine Learning

0 3 75 0

Exercise: Machine Learning

0 1 45 0

Teaching Unit / Examinations: Examination Machine Learning

Title Examination Machine Learning

Sub-title Exa ML

Semester 1


Compulsory Module

Teaching Unit / Examinations: Lecture Machine Learning

Title Lecture Machine Learning

Sub-title L ML

Semester 1


Compulsory Module

Teaching Unit / Examinations: Exercise Machine Learning

Title Exercise Machine Learning

Sub-title E ML

Semester 1

20


Compulsory Module

21

Module: Computer Science in Mechanical Engineering II

Module Computer Science in Mechanical Engineering II

Module level Master

Subtitle CSME II


Semester 1

Person in charge Prof. Dr.-Ing. Tobias Meisen

Lecturer Prof. Dr.-Ing. Tobias Meisen

Language English


Compulsory Module

Teaching form Written examination, Lecture, Exercise


Lecture hours 60h


5


-none-

Learning objectives

Computer Science in Mechanical Engineering II

Overall goal: Students gain the basic knowledge about computational methods in probabilistic robotics, which focus on popular algorithms from theory to implementation. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

obtain a comprehensive view of the current challenges in

development of mechatronic systems, which focuses on mobile and

stationary robotics;

are familiar with the fundamental concepts, tools and theories of

computational methods;

understand how to analyse and solve the interrelated problems of

computer science using these methods with practical consideration.

22

Abilities / Skills

Students

learn how to use the acquired methods in mechatronic systems for

different tasks correctly;

can identify the advantages and disadvantages of the various

procedures and assess them in a practical context.

Competencies

Students

find solutions for action planning in discrete and continue action

space;

find solutions for navigation in non-structured environment;

take advantages of probabilistic analysis for system state estimation

from noisy data;

design model based and learnable decision-making processes.

Content

Computer Science in Mechanical Engineering II

General introduction of intelligent mechatronic system

Noninformative and informative searching algorithms

Theory of probabilistic modelling

System state estimation I: Kalman Filter and nonlinear Kalman Filter

System state estimation II: Unscented Kalman Filter and Particle Filter

Decision-making processes

Trajectory optimization

Reinforcement Learning for mechatronic systems

Supervised and Unsupervised Learning for mechatronic systems

Tendency of Research of intelligent mechatronic system





Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Computer Science in Mechanical Engineering II

5 0 0 120

Lecture: Computer Science in Mechanical Engineering II

0 2 45 0

23

Exercise: Computer Science in Mechanical Engineering II

0 2 45 0

Teaching Unit / Examinations: Computer Science in Mechanical Engineering II

Title Examination Computer Science in Mechanical Engineering II

Sub-title Exa CSME II

Semester 1


Compulsory Module

Teaching Unit / Examinations: Lecture Computer Science in Mechanical Engineering II

Title Lecture Computer Science in Mechanical Engineering II

Sub-title L CSME II

Semester 1


Compulsory Module

Teaching Unit / Examinations: Exercise Computer Science in Mechanical Engineering II

Title Exercise Computer Science in Mechanical Engineering II

Sub-title E CSME II

Semester 1


Compulsory Module

24

Module: German Language Course

Module German Language Course

Module level Master

Subtitle GLC


Semester 1

Person in charge -

Lecturer -

Language German

Assignment to the

curriculum Compulsory Module

Teaching form

Workshops to teach skills, with practice sessions; Self-study, Group

exercises; Action-learning, based on role-plays, simulations and

behavioural exercises

Grading Options:

a) Written exam (60-120 min., graded, 100%) or oral exam (15-45 min., graded, 100%)

b) Written exam (60-120 min., graded, 50%) and oral exam (15-45 min., graded, 50%)


Lecture hours 30h

ECTS-Credit Points

(CP) 2

Requirements

according to

examination

regulation

-none-

Learning objectives

German Language Course

After successfully completing this course, the students will have acquired

the following learning outcomes:

Abilities / Skills

Students

communicate basic knowledge

25

on German Culture and Cultural Studies;

accomplish everyday

communication within university surroundings

(dormitory, cafeteria etc.);

offer prerequisites for culturally

adequate application documents for internships

(CV, letter of motivation).

Content

German Language Course

Getting to know someone

Introducing oneself

City explorations

Orientation in the city

Techniques: learning and remembering words

Buying groceries

Communication on the phone

Techniques: learning grammar systematically

Calendar, festivities

Holidays

Learning and forgetting

Learning psychology

German newspapers

Reading habits

When in Rome, do as the Romans do

Intercultural experience

Media

Geographic German studies

Inventions and progress

Between cultures

Environmental protection/problems

Project Europe

Job market Germany

Applications

CVs




26

Teaching Unit / Examinations:

Title Code Credit

Points

Workload (SWS / h)

Duration of

Exam (min) Lecture h.

(SWS)

Self-Study

(h)

Examination:

German Language

Course

2 0 0

See

examination

options

Lecture:

German Language

Course

0 1 15 0

Exercise:

German Language

Course

0 1 15 0

Teaching Unit / Examinations: Examination German Language Course

Title Examination German Language Course

Sub-title Exa GLC

Semester 1

Connection to the


Teaching Unit / Examinations: Lecture German Language Course

Title Lecture German Language Course

Sub-title L GLC

Semester 1

Connection to the


Teaching Unit / Examinations: Exercise German Language Course

Title Exercise German Language Course

Sub-title E GLC

Semester 1

Connection to the


27

Compulsory Courses – Second Term

Module: Multibody Dynamics

Module Multibody Dynamics

Module level Master

Subtitle MBD


Semester 2

Person in charge Univ.-Prof. Dr.-Ing. Dr. h. c. (UPT) Burkhard Corves

Lecturer Univ.-Prof. Dr.-Ing. Dr. h. c. (UPT) Burkhard Corves

Language English


Compulsory Module


Workload Total 150h, Lecture hours 60h , Self-study 90h

Lecture hours 60h


5


none

Learning objectives

Multibody Dynamics

Overall goal: The students have a profound comprehension of the fundamentals of multibody dynamics as well as the behaviour of vibratory mechanical systems. Thus, they are capable of comprehending, describing and analysing oscillation systems. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

have a profound comprehension of the fundamentals of multibody

dynamics as well as the behaviour of vibratory mechanical systems;

are capable of comprehending, describing and analysing oscillation

systems;

28

are familiar with the most important methods for calculating the

eigen-behaviour and the behaviour under force excitation for linear

oscillatory systems.


Students

are capable of modelling and mathematically describing mechanical

oscillation systems in consideration of physical effects such as

elasticities, damping, friction, etc.;

form and linearize the non-linear equations of motion;

deduce the necessary methods and procedures from their acquired

knowledge for the synthesis and analysis of the analysed oscillatory

systems;

answer and solve comprehensive problems on the selection and

design of industrial vibratory systems;

interpret the results of calculations and simulations in a meaningful

way, especially considering possible simplifications in the performed

modelling.

Content

Multibody Dynamics

1 System modelling

• Specification of the systems components and physical effects

• Methods of approach for equivalent models

• Multibody systems

• Determination of the model parameters

• Mathematical description of oscillatory systems

2 Kinematics of multibody systems

• Position and orientation of bodies

• Translational kinematics

• Rotational kinematics

3 Equations of motion in minimal coordinates

• Newton-euler equations

• Lagrangian equations of 2nd kind

4 Equations of motion in absolute coordinates

• Position description of a body in absolute coordinates

• Constraints and constraint forces

• Setting up the equation of motion

• Transfer of the DAE to ODE

5 Equations of motion of linear oscillation systems

• Linearization of equations of motion using taylor expansion

• Solution of linear equations of motion using the eigenvalue approach

• Linear mechanical systems with harmonic excitation

29

6 Setting up the state equations

• Common mechanical systems

• General mechanical systems

7 State equations of linear oscillation systems

• Solution of the state equations with the eigenvalue formulation

• Solution of the state equation of linear time-invariant vibrating systems by means of the fundamental matrix

• Comparison of the fundamental matrix solution statement with the method of eigenvalues

• Linear mechanical systems with step excitation

• Linear mechanical systems with harmonic excitation

• Linear mechanical systems with periodical excitation

Media e-Learning L2P (Moodle), Power Point




Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Multibody Dynamics 5 0 0 120

Lecture: Multibody Dynamics

0 2 45 0

Exercise: Multibody Dynamics

0 2 45 0

Teaching Unit / Examinations: Examination Multibody Dynamics

Title Examination Multibody Dynamics

Sub-title Exa MBD

Semester 2


Compulsory Module

Teaching Unit / Examinations: Lecture Multibody Dynamics

Title Lecture Multibody Dynamics

Sub-title L MBD

Semester 2

30


Compulsory Module

Teaching Unit / Examinations: Exercise Multibody Dynamics

Title Exercise Multibody Dynamics

Sub-title E MBD

Semester 2


Compulsory Module

31

Module: Computer Vision I

Module Computer Vision I

Module level Master

Subtitle CV I


Semester 2



Language English


Compulsory Module



Lecture hours 60h


6


It is advised to have knowledge in Linear algebra Basic and probability theory and statistics

Learning objectives

Computer Vision I

Overall goal: The goal of Computer Vision is to develop methods that enable a machine to analyze and "understand" the content of images and videos. This lecture teaches the fundamental Computer Vision techniques that underlie such capabilities. In addition, it shows current research developments and how they are applied to solve real-world tasks. The detailed lecture topics include After successfully completing this course, the students will have acquired the following learning outcomes:


Students

have an extended knowledge in image processing: binary image

processing, linear filters, image derivatives, image pyramids, edge

detection, segmentation, graph theoretic segmentation, normalized

cuts, graph cut segmentation;

32

have an extended knowledge in object recognition and

categorization: histogram-based representations, distance

measures, Eigenfaces, Fisherfaces, sliding-window object detection;

have an extended knowledge in local feature extraction: Harris and

Hessian interest points, Laplacian scale selection, affine covariant

interest regions, SIFT descriptors;

have an extended knowledge in image matching and retrieval: visual

vocabularies, bag-of-words approaches, inverted file indexing,

vocabulary tree, homography verification;

have an extended knowledge in 3D reconstruction: epipolar

geometry, stereo reconstruction, structure-from motion;

have an extended knowledge in motion estimation: Lukas-Kanade

optical flowM

have an extended knowledge in tracking: Kalman filters, linear

dynamic models.

line and circle fitting;

have an extended knowledge in segmentation: segmentation by

clustering, k-Means, EM clustering, mean-shift clustering.

Abilities / Skills

Students

derive, explain, and apply the following practical computer vision algorithms:

Image processing: Thresholding, morphology operators, image derivatives, Canny

edge detection, Hough transform for line and circle detection

Mean-shift segmentation

Histogram-based object recognition, face recognition with Eigenfaces and

Fisherfaces, Viola-Jones face detection

Local feature extraction: Harris and Hessian interest point extraction, Laplacian scale

selection, homography estimation

3D reconstruction: Fundamental matrix estimation with the Eight-point algorithm, DLT

triangulation, RANSAC

Lucas-Kanade optical flow computation

Object tracking with Kalman filters

Competencies

Students

discuss the advantages and disadvantages of the covered computer

vision techniques:

find practical solutions to complex real-world computer vision

problems:


33

Content

Computer Vision I

The goal of Computer Vision is to develop methods that enable a machine to analyze and "understand" the content of images and videos. This lecture teaches the fundamental Computer Vision techniques that underlie such capabilities. In addition, it shows current research developments and how they are applied to solve real-world tasks. The detailed lecture topics include

Image Processing Basics: The image formation process, binary image processing, linear filters, edge detection, structure extraction, radiometry, color

Image Segmentation: Segmentation as clustering, k-means, EM, mean-shift, segmentation as energy minimization, normalized cuts, graph cuts.

Object Recognition: Histogram based approaches, subspace representations

Local Invariant Features: Invariant feature extraction, local descriptors, efficient matching and indexing, recognition with local features

Object Categorization: Sliding-window approaches, Bag-of-visual-word approaches, part-based approaches

3D Reconstruction: Epipolar geometry, camera calibration, multi-view stereo, structure-from-motion

Motion & Tracking: Optical flow, tracking with linear dynamic models, Kalman filter


Literature

D. Forsyth, J. Ponce, Computer Vision -- A Modern Approach, Prentice Hall, 2002.

R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision, 2nd Edition, Cambridge University Press, 2004.

K. Grauman, B. Leibe, Visual Object Recognition, Morgan & Kaufman publishers, 2011.


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination:Computer Vision I

6 0 0 90

Lecture: Computer Vision I

0 3 75 0

Exercise: Computer Vision I

0 1 45 0

Teaching Unit / Examinations: Examination COmputer Vision I

Title Examination Computer Vision I

Sub-title Exa CV I

34

Semester 2


Compulsory Module

Teaching Unit / Examinations: Lecture Computer Vision I

Title Lecture Computer Vision I

Sub-title L CV I

Semester 2


Compulsory Module

Teaching Unit / Examinations: Exercise Computer Vision I

Title Exercise Computer Vision I

Sub-title E CV I

Semester 2


Compulsory Module

35

Module: Robotic Sensor Systems

Module Robotic Sensor Systems

Module level Master

Subtitle RSS


Semester 2

Person in charge Univ.-Prof. Dr.-Ing. Robert Schmitt

Lecturer Univ.-Prof. Dr.-Ing. Robert Schmitt

Language English


Compulsory Module



Lecture hours 60h


5


-none-

Learning objectives

Robotic Sensor Systems

Overall goal: The aim is to familiarize the students with the different sensor systems a robot can contain, how these sensor work and why the robot needs these sensors. It is distinguished between internal and external sensors. In addition, some basics about signal transmission and signal processing are introduced. Knowledge / Understanding

Students

know a large choice of internal and external sensors of a robot;

know work principles of all introduced kinds of sensor systems;

know the basics about signal processing and transmission,

containing the corresponding mathematical and physical principles.

Abilities / Skills

Students

36

calculate the signal from recording it with the sensor to processing

transmitting it;

explain how the sensor systems of a robot work and what they are

applied for.

Competencies

Students

know which sensor system is indispensable to give the robot special

skills and properties.

Content

Robotic Sensor Systems

Internal sensors (Position, speed, acceleration sensors, internal navigation systems)

External sensors (Tactile, proximitx, distance, position and visual sensors)

Basics about signal transmittance and processing

Special applications (space, Fukushima, under water)

Examples at IPT and WZL

Media L²P, PowerPoint-slides

Literature Lecture and exercise slides containing references


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Robotic Sensor Systems

5 0 0 120

Lecture: Robotic Sensor Systems

0 2 60 0

Exercise: Robotic Sensor Systems

0 2 60 0

Teaching Unit / Examinations: Examination Robotic Sensor Systems

Title Examination Robotic Sensor Systems

Sub-title Exa RSS

Semester 2

37


Compulsory Module

Teaching Unit / Examinations: Lecture Robotic Sensor Systems

Title Lecture Robotic Sensor Systems

Sub-title L RSS

Semester 2


Compulsory Module

Teaching Unit / Examinations: Exercise Robotic Sensor Systems

Title Exercise Robotic Sensor Systems

Sub-title E RSS

Semester 2


Compulsory Module

38

Elective Courses – Second Term

Module: Computer Vision II

Module Computer Vision II

Module level Master

Subtitle CV II


Semester 2



Language English


Elective Module



Lecture hours 60h


6


Basic knowledge of linear algebra, Basic knowledge of probability theory and statistics.

Lecture Computer Vision, Lecture Machine Learning

Learning objectives

Computer Vision II

After successfully completing this course, the students will have acquired the following learning outcomes:


Students

know the theoretical foundations underlying Computer Vision techniques in the areas of:

Motion estimation, Optical Flow

Background modeling

Single-object tracking

Dynamic models

39

Multi-object tracking

Articulated body estimation and articulated tracking

Abilities / Skills

Students

derive, explain and apply the following computer vision algorithms:

Motion estimation: Lukas-Kanade optical flow estimation Background modeling: Adaptive mixture-of-Gaussian models, Kernel models

Single-object tracking: Generalized Lukas-Kanade template tracking, mean-shift tracking, active contours, tracking by online classification, tracking by detection

Dynamic models: Kalman filters, Particle filters

Multi-object tracking: multi-hypothesis data association, Network flow optimization

Articulated body pose estimation: Gaussian Process pose estimation, model-based tracking, Active Appearance Models, Pictorial Structures

Competencies

Students

discuss the advantages and disadvantages of the covered computer

vision techniques;

find practical solutions to complex real-world computer vision

problems;


Content

Computer Vision II

The lecture will cover advanced topics in computer vision. A particular focus will be on state-of-the-art techniques for object detection, tracking, and body pose estimation. The detailed lecture topics include

Motion estimation, Optical Flow

Background modeling

Single-object tracking

Dynamic models

Multi-object tracking

Articulated body estimation and articulated tracking


Literature

Lecture script in form of printed slides Additional handouts for certain topics Web page with supplementary material and exercises:

http://www.vision.rwth-aachen.de/teaching Research papers and tutorials covering the state-of-the-art

algorithms will be made available to the students.


40

Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Computer Vision II

6 0 0 90

Lecture: Computer Vision II

0 3 60 0

Exercise: Computer Vision II

0 1 60 0

Teaching Unit / Examinations: Examination Computer Vision II

Title Examination Computer Vision II

Sub-title Exa CV II

Semester 2


Elective Module

Teaching Unit / Examinations: Lecture Computer Vision II

Title Lecture Computer Vision II

Sub-title L CV II

Semester 2


Elective Module

Teaching Unit / Examinations: Exercise Computer Vision II

Title Exercise Computer Vision II

Sub-title E CV II

Semester 2


Elective Module

41

Module: Production Metrology

Module Production Metrology

Module level Master

Subtitle PM


Semester 2

Person in charge Univ.-Prof. Dr.-Ing. Robert Schmitt

Lecturer Univ.-Prof. Dr.-Ing. Robert Schmitt

Language English


Elective Module


Workload Total 150h, Lecture hours 60h, Self-study 90sh

Lecture hours 60h


5


none

Learning objectives

Production Metrology

Overall goal: Students create the awareness, that “measuring” comprehends a lot more than plain data acquisition and metrology is a vital part of modern production processes. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

know the function and the responsibility of metrology for production;

know the theoretical fundamentals which have to be taken into

consideration while the measuring process is planned, controlled,

analysed, are discussed;

know current measuring principles and devices in the field of

industrial production;

42

know statistical fundamentals being necessary for analysis of the

measured values.

Abilities / Skills

Students

define measuring task on the basis of given features;

select adequate measuring devices for measuring tasks;

interpret measuring results.

Competencies

Students

make their decision (having arguments) for using metrology within

production;

make decisions concerning measurement on the base of different

parameters.

Content

Production Metrology

Introduction Relevance of metrology for quality assurance and its integration in

production processes.

Metrological Basics Metrological concepts and definitions (Calibration, Uncertainty

etc.)

Tolerancing Form and positional tolerances, tolerancing principles and basics

Inspection Planning

Tasks and workflow of inspection planning, Procedure for creation of inspection plans

Shop floor measuring devices/ Measuring sensors

Commonly used manual inspection devices for the shop floor, Function and application of inductive, capacitive and pneumatical sensors

Optoelectronic inspection devices

Optical inspection systems for geometry testing and applications

Form and surface inspection devices Tactile and optical system for the characterisation of forms and

surfaces, surfaces parameters

Coordinate measurement technology Principles, types and applications of coordinate measuring

machines

Gauging inspection Form and positional gauging, Gauging Procedures

Statistical basics Statistical parameters for the description of production and measuring processes, tests on normal distribution

43

SPC, Process Capability

Statistical analysis and control of processes, Process capability indices

Inspection device management Tasks and procedures of inspection device management, Calculation of measuring device capability, Calibration chain


Literature Lecture script in form of printed slides


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Production Metrology

5 0 0 120

Lecture: Production Metrology

0 2 45 0

Exercise: Production Metrology

0 2 45 0

Teaching Unit / Examinations: Examination Production Metrology

Title Examination Production Metrology

Sub-title Exa PM

Semester 2


Elective Module

Teaching Unit / Examinations: Lecture Production Metrology

Title Lecture PM

Sub-title L PM

Semester 2


Elective Module

Teaching Unit / Examinations: Exercise Production Metrology

Title Exercise Production Metrology

Sub-title E PM

Semester 2

44


Elective Module

45

Module: Machine Dynamics of Rigid Systems

Module Machine Dynamics of Rigid Systems

Module level Master

Subtitle MDRS


Semester 2

Person in charge apl. Prof. Dr.-Ing. Mathias Hüsing

Lecturer apl. Prof. Dr.-Ing. Mathias Hüsing

Language English


Elective Module

Teaching form Written examination, Lecture, Exercise


Lecture hours 60h


6


none

Learning objectives

Machine Dynamics of Rigid Systems

Overall goal: Students gain the basic knowledge about machine dynamics and the fundamental means for mass balancing and power smoothing. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

know the fundamental means for mass balancing and power

smoothing of single slider reciprocating machines and other general

mechanical systems;

know about the basic relations, resulting in fluctuating angular

velocities due to varying mass moments of inertia and varying loads

as reduced to a reference shaft. The relations can be derived and

explained.

46

Abilities / Skills

Students

explain and derive the mass forces and mass moments of single and

multi slider reciprocating machines.

Competencies

Students

derive the influencing factors for fluctuating speeds in single and

multi slider reciprocating machines can be described. Based on that

potential means for power smoothing;

develop the required kinematic and dynamic relations for the

machines and mechanisms under investigation;

Moreover, balancing of machines and mechanisms can be

performed with high mass forces, including design issues and

mathematical derivations;

develop practical and innovative instructions for mass balancing and

power smoothing from the dynamic analyses;

gain fundamental knowledge that can be applied to related industrial

challenges (including special machine construction and

specifications) in the field of design improvement by means of mass

balancing and power smoothing.

Content

Machine Dynamics of Rigid Systems

1 introduction / basic principles / planar kinematics and dynamics of rigid bodies

2 dynamic force analysis of planar mechanisms with rigid links: graphical technique / analytical approach

3 dynamic motion analysis of planar mechanisms with rigid links (neglecting friction)

4 kinematics and dynamics in single slider reciprocating machines: dynamically equivalent system of connecting rod / determination of frame torque

5 mass balancing for single slider reciprocating machines: determination / balancing of inertia forces & determination / balancing of inertial moments

6 mass balancing for multi slider reciprocating machines: determination (incl. graphical approach) / balancing of inertia forces & determination / balancing of inertial moments

7 introduction into power smoothing in mechanisms and slider reciprocating machines

8 equations of motion: external forces and moments / kinetic energy / potential energy

47

9 solution of equation of motion: general / for constant mass moment of inertia / for constant angular velocity / for specified instantaneous speed and acceleration / for constant energy

10 fluctuation of angular velocity / non uniformity factor

11 influence of flywheel on angular velocity & analytical / approximative calculation of flywheel

Media e-Learning L2P (Moodle), Power Point

Literature

Lecture Notes

Dresig, H.; Holzweißig, F.: Maschinendynamik / VDI-Richtlinie 2149: Getriebedynamik (Fachausschuss A204, Ltng. Prof. Dresig) Blatt 1: Starrkörper-Mechanismen / Dresig, H.: Schwingungen mechanischer Antriebssysteme / Gasch, R.; Nordemann, R.; Pfützner, H.: Rotordynamik / Pfeiffer, F.: Einführung in die Dynamik / Magnus, K.; Popp, K.: Schwingungen / Heimann, B.; Gerth, W.; Popp, K.: Mechatronik / Ulbrich, H: Maschinendynamik


Title

Workload (SWS / h)

Lecture h. (SWS)

Self-Study (h)

Examination: Machine Dynamics of Rigid Systems

6 0 0 120

Lecture: Machine Dynamics of Rigid Systems

0 2 60 0

Exercise: Machine Dynamics of Rigid Systems

0 2 60 0

Teaching Unit / Examinations: Examination Machine Dynamics of Rigid Systems

Title Examination Machine Dynamics of Rigid Systems

Sub-title Exa MDRS

Semester 2


Elective Module

Teaching Unit / Examinations: Lecture Machine Dynamics of Rigid Systems

Title Lecture Machine Dynamics of Rigid Systems

Sub-title L MDRS

Semester 2

48


Elective Module

Teaching Unit / Examinations: Exercise Machine Dynamics of Rigid Systems

Title Exercise Machine Dynamics of Rigid Systems

Sub-title E MDRS

Semester 2


Elective Module

49

Module: Industrial Logistics

Module Industrial Logistics

Module level Master

Subtitle IL


Semester allocation 2

Person in charge Univ.-Prof. Dr.-Ing. Dipl.-Wirt. Ing. Günther Schuh, apl. Prof. Dr.-Ing.

Volker Stich

Lecturer Univ.-Prof. Dr.-Ing. Dipl.-Wirt. Ing. Günther Schuh, apl. Prof. Dr.-Ing.

Volker Stich

Language English

Assignment to the

curriculum Elective Module


Workload Total 150h, Lecture Hours 45h, Self-study 105h

Lecture hours 45h

ECTS-Credit Points

(CP) 5

Requirements

according to

examination

regulation

none

Learning objectives

Industrial Logistics



Students

know objectives and tasks of industrial logistics;

know main aspects of industrial logistics from organisational

involvement to logistics controlling;

understand the meaning and the effects of individual aspects of

industrial logistics.

Abilities / Skills

Students

place knowledge of industrial logistics in the overall context;

apply knowledge acquired to practical problems.

Content Industrial Logistics

50

• Objectives and tasks of logistics

• Organisational involvement of logistics

• Exercise: Prozess optimisation

• Material flow design

• Recitation by an external

• Information logistics

• Exercise: ''Beergame''

• Development and Procurement

• Exercise: Development and Procurement

• Material and finished goods disposition

• Exercise: Workshop on the Enhancement of Disposition Quality

• Distribution logistics

• Exercise: Opening proceedings for tour planning

• Spare part logistics

• Recitation by an external

• Logistics controlling

• Exercise: ABC, XYZ Analysis





Title ECTS

Workload (h) Duration

of Exam

(min) Lecture h.

(SWS) Self-Study (h)

Examination:

Industrial Logistics 5 0 0 120

Lecture:


Exercise:


Teaching Unit / Examinations: Examination Industrial Logistics

Title Examination Industrial Logistics

Sub-title Exa IL

51


Connection to the


Teaching Unit / Examinations: Lecture Industrial Logistics

Title Lecture Industrial Logistics

Sub-title L IL


Connection to the


Teaching Unit / Examinations: Exercise Industrial Logistics

Title Exercise Industrial Logistics

Sub-title E IL


Connection to the


52

Module: Artificial Intelligence and Data Analytics for Engineers

Module Artificial Intelligence and Data Analytics for Engineers

Module level Master

Subtitle AIDAE


Semester 2

Person in charge Prof. Dr.-Ing. Tobias Meisen

Lecturer Prof. Dr.-Ing. Tobias Meisen

Language English


Elective Module



Lecture hours 60h


5


none

Learning objectives

Artificial Intelligence and Data Analytics for Engineers

Overall goal: Students gain the application-specific knowledge about artificial intelligence (especially: machine learning as supervised, unsupervised and reinforcement learning) and data analytics (especially: data exploration, data mining, data visualization and interpretation of analysis results) for application in the engineering domain. After successfully completing this course, the students will have achieved the following learning outcomes:


Students

obtain a comprehensive view of the challenges in the application and

usage of artificial intelligence and data analytics in the engineering

domain;

are familiar with fundamental concepts and methods of machine

learning and data mining in the engineering domain;

53

know and understand the different steps (cleansing, transformation

and extraction) necessary to analyse and to use data in various

engineering scenarios;

know about the application scope of specific methods and their

strength as well as their limits;

obtain a view on specific evaluation methods with regards to the

choice of analysis method and the underlying data;

are familiar with the intricacies of interpreting analysis results with

regards to the utilized analysis methods and evaluation method.

Abilities / Skills

Students

learn how to use and apply the methods and concepts in engineering

tasks correctly;

learn to distinguish between different analysis and learning scenarios

and how to approach engineering related challenges;

learn the basics of state of the art tools that are used for AI data

analytics in the engineering domain;

learn to choose the appropriate tools for the different steps of the

knowledge discovery and artificial learning process.

Competencies

Students

independently evaluate analysis scenarios in the engineering context

and select suitable methods accordingly;

find solutions for different analysis scenarios;

have practical and applicable knowledge about data analytics and

machine learning for engineering purposes;

use tools used in the machine learning and data analytics domain

and reflect the usage;

can combine single tools into a toolchain for an analysis pipeline to

address complex problems in the engineering domain.

Content

Artificial Intelligence and Data Analytics for Engineers

Introduction to Data Analytics and Artificial Intelligence in

Engineering: Goals, Challenges, Obstacles, and Processes

Data Preparation: Cleansing and Transformation

Data Integration: Architectures, Challenges, and Approaches

Data Representation: Feature Extraction and Selection

Data-Driven Learning: Supervised (Classification, Regression) and

Unsupervised Learning (Clustering) for Engineers

State-of-the-Art Methods: Reinforcement Learning and Deep Neural

Networks (GANs, CNNs, Restricted Boltzman Machines etc.)

Data Mining and Visual Analytics

Media e-Learning L2P, Power Point, Real-World-Applications and Data

Literature Lecture Notes; Students also receive a list of relevant literature

54


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Artificial Intelligence and Data Analytics for Engineers

5 0 0 90

Lecture: Artificial Intelligence and Data Analytics for Engineers

0 2 45 0

Exercise: Artificial Intelligence and Data Analytics for Engineers

0 2 45 0

Teaching Unit / Examinations: Examination Artificial Intelligence and Data Analytics for Engineers

Title Examination Artificial Intelligence and Data Analytics for Engineers

Sub-title Exa AIDA

Semester 2


Elective Module

Teaching Unit / Examinations: Lecture Artificial Intelligence and Data Analytics for Engineers

Title Lecture Artificial Intelligence and Data Analytics for Engineers

Sub-title L AIDA

Semester 2


Elective Module

Teaching Unit / Examinations: Exercise Artificial Intelligence and Data Analytics for Engineers

Title Exercise Data Analytics

Sub-title E AIDA

Semester 2


Elective Module

55

Module: Factory Planning

Module Factory Planning

Module level Master

Subtitle FaPl


Semester 2

Person in charge Univ.-Prof. Dr.-Ing. Achim Kampker

Lecturer Univ.-Prof. Dr.-Ing. Achim Kampker

Language English


Elective Module



Lecture hours 4


5


-none-

Learning objectives

Factory Planning

Dear factory planer, design a factory which can produce watches today and cars tomorrow, that can produce different volumes each day, which is inflatable and transportable (Helmut Schulte).

The global competition, wide production programmes und frequent discontinuities lead to so far unknown challenges for the planning process of factories. Besides the classical resource, layout and logistic planning, also the definition of the own value adding scope, the choice and allocation of suitable production locations, the conception of production systems and the usage of suitable planning tools, are part of the process.

The lecture factory planning shows the state of the art of the particular topics, best-practice methods and approaches are explained and reference solutions presented. The theoretical content is deepened by an accompanying case-study and the presentation of actual industrial factory planning projects.


56


Students

have an extended understanding of state of the art planning process

of factories;

know and understand the definition of the own value adding scope,

the choice and allocation of suitable production locations, the

conception of production systems and the usage of suitable planning

tools.

Abilities / Skills

Students

apply this knowledge to analyse organizational structures and forms

of production.

Competencies

Students

define and develop single production plants as well as production

networks of globalized companies and explain them to different

target groups

Content

Factory Planning

L1/L2 - Introduction

Comprehending the basic glossary, getting to know the content and understanding the challenges and requirements of modern factory planning.

L3/L4 - Dimensions of added value in Production / Evaluation methods for the planning process of value added

Getting to know different categories of value added in factory planning as well as strategic and economic methods for their evaluation

L5/L6 - Production site planning

This lecture focusses on current trends within the field of production site planning and presents methods for the assessment of production site alternatives and decision-making

L7/8 – Production Systems I: Process Planning and Resource Planning

Learning about challenges and approaches within the production process planning, understanding the problem of capacity planning in manufacturing and human resources

L9/10 - Production Systems II: Organization and Lean Production

Introduction to different organizational structures and forms of production, comprehending lean production with its basic elements and understanding the implementation of lean principles into production systems

L11/12 - Logistics planning

57

Comprehend the basics of logistics planning, getting to know the development of logistic strategies and principles from sourcing to recycling processes

L13/L14 - Layout and factory structure planning

Introduction to challenges and targets of layout and factory structure planning. Acquiring knowledge of design and assessment of factory layouts

Media e-Learning L2P, Power Point, group work




Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Factory Planning

6 0 0 120

Lecture: Factory Planning

0 2 60 0

Exercise: Factory Planning

0 2 60 0

Teaching Unit / Examinations: Examination Factory Planning

Title Examination Factory Planning

Sub-title Exam FaPl

Semester 2


Elective Module

Teaching Unit / Examinations: Lecture Factory Planning

Title Lecture Factory Planning

Sub-title L FaPl

Semester 2/4


Elective Module

Teaching Unit / Examinations: Exercise Factory Planning

Title Exercise Factory Planning

58

Sub-title E FaPl

Semester 2


Elective Module

59

Module: Summer School – Advanced Topics in Robotic Systems

Engineering

Module Summer School – Advanced Topics in Robotic Systems Engineering

Module level Master

Subtitle SuS


Semester 2

Language English


Elective Module



3


none

Content Students can choose a Summer School worth up to 3 CP

Media -

Literature -


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Summer School

3 0 0 60

Lecture: Summer School

0 2 60 0

Exercise: Summer School

0 2 60 0

Teaching Unit / Examinations: Examination Summer School

Title Examination Summer School – Advanced Topics in Robotic Systems Engineering

Sub-title Exa SuS

60

Semester 2


Elective Module

Teaching Unit / Examinations: Lecture Summer School

Title Lecture Summer School – Advanced Topics in Robotic Systems Engineering

Sub-title L SuS

Semester 2


Elective Module

Teaching Unit / Examinations: Exercise Summer School

Title Exercise Summer School – Advanced Topics in Robotic Systems Engineering

Sub-title E SuS

Semester 2


Elective Module

61

Compulsory Course – Third Term

Module: Simulation of Robotic Systems, Sensors and Environment

Module Simulation of Robotic Systems, Sensors and Environment

Module level Master

Subtitle SRSE


Semester 3

Person in charge Univ.-Prof. Dr.-Ing. Jürgen Roßmann

Lecturer Univ.-Prof. Dr.-Ing. Jürgen Roßmann

Language English


Compulsory Module



Lecture hours 45h


5


none

Learning objectives

Simulation of Robotic Systems, Sensors and Environment

Overall goal: Students gain the basic knowledge concerning methods and processes to simulate robotic systems in their operational environment and to use such simulations throughout the life-cycle of the robot. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

understand why simulation of robotic systems is important for robot

engineering and operation;

understand the Digital Twin concept and its relationship to

engineering and real-world operation;

62

are familiar with the most important simulation methods used for the

simulation of robots, their sensors and actuators as well as their

dynamic environment;

know how to use these methods in different usage scenarios.

Abilities / Skills

Students

use simulation technology to realize Digital Twins of robot

manipulators, mobile robots, working machines etc. in different

application areas (factory, space, construction, forestry);

analyse and understand the simulation results.

Competencies

Students

select and combine appropriate simulation methods for Digital Twins

in different usage scenarios;

use and integrate Digital Twins in engineering processes and robot

operation;

understand and present the simulation results.

Content

Simulation of Robotic Systems, Sensors and Environment

General introduction: Simulation is the “representation of a system with its dynamic processes in an experimentable model to reach findings which are transferable to reality.” State-of-the-art simulation technology makes this possible even for complex networks of interacting Digital Twins. This makes simulation indispensable both for the development and operation of automation systems.

Terminology and basic concepts: (Technical) asset, system, model, simulation, simulator, verification, validation, calibration, adjustment

Requirements for simulation technology

The Digital Twin concept

Simulation and Industry 4.0

The Virtual Testbed concept

Use of simulation in engineering processes in different application areas

Multi-disciplinary simulation for multi-disciplinary systems

Classification and comparison of simulation methods

Domain-independent simulation methods, e.g. equation-based simulation, signal-oriented simulation, object-oriented simulation, discrete-event simulation, agent-based simulation

Domain-specific simulation methods for robotics, e.g. Kinematics (orientation, pose, transformation, kinematic chains and trees, forward and inverse kinematics), equations of motion (Newton Euler, Lagrange), rigid body dynamics, sensor simulation

63

Coupling of simulation models and simulators

Simulation and the sense-think-act cycle

Simulation and model-based systems engineering

Digital Factory

Virtual Commissioning

Integration of simulation technology in engineering processes

Data management and data formats

Semantic world modelling

Simulation and optimization

Simulation and man-machine-interaction





Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Simulation of Robotic Systems, Sensors and Environment

5 0 0 30 (oral), 60-120 (written)

Lecture: Simulation of Robotic Systems, Sensors and Environment

0 2 45 0

Exercise: Simulation of Robotic Systems, Sensors and Environment

0 1 60 0

Teaching Unit / Examinations: Examination Simulation of Robotic Systems, Sensors and Environment

Title Examination Simulation of Robotic Systems, Sensors and Environment

Sub-title Exa SRSE

Semester 3


Compulsory Module

Teaching Unit / Examinations: Lecture Simulation of Robotic Systems, Sensors and Environment

64

Title Lecture Simulation of Robotic Systems, Sensors and Environment

Sub-title L SRSE

Semester 3


Compulsory Module

Teaching Unit / Examinations: Exercise Simulation of Robotic Systems, Sensors and Environment

Title Exercise Simulation of Robotic Systems, Sensors and Environment

Sub-title E SRSE

Semester 3


Compulsory Module

65

Elective Courses – Third Term

Module: Introduction to Artificial Intelligence

Module Introduction to Artificial Intelligence

Module level Master

Subtitle IAI


Semester 3

Person in charge Univ.-Prof. Gerhard Lakemeyer, Ph. D.

Lecturer Univ.-Prof. Gerhard Lakemeyer, Ph. D.

Language English


Elective Module



Lecture hours 60h


6


Basic knowledge of computer science

Learning objectives

Introduction to Artificial Intelligence

Overall goal: The aim is to familiarize the student with basic concepts and methods of Artificial Intelligence and to enable the student to apply them in practice. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

are familiar with the methods underlying the design of intelligent

agents, including search methods, knowledge representation using

first-order logic, planning, reasoning under uncertainty, and inductive

learning.

66

Abilities / Skills

Students

apply the methods taught in class to design intelligent agents him- or

herself.

Competencies

Students

identify components and functionalities, which call for the use of

Artificial Intelligence methods, and adapt and implement those

methods for such purposes, when developing large software

systems.

Content

Introduction to Artificial Intelligence

Agent Architecture

Heuristic Search

Games

Knowledge Representation

Baysian Networks

Machine Learning

Robotics

Media L2P, LaTeX Slides

Literature Lecture Notes (Transparencies) Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern

Approach (2nd Edition), Addison Wesley, 2002.


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Introduction to Artificial Intelligence

6 0 0 120

Lecture: Introduction to Artificial Intelligence

0 2 60 0

Exercise: Introduction to Artificial Intelligence

0 2 60 0

Teaching Unit / Examinations: Examination Introduction to Artificial Intelligence

Title Examination Introduction to Artificial Intelligence

Sub-title Exa IAI

Semester 3

67


Elective Module

Teaching Unit / Examinations: Lecture Introduction to Artificial Intelligence

Title Lecture Introduction to Artificial Intelligence

Sub-title L IAI

Semester 3


Elective Module

Teaching Unit / Examinations: Exercise Introduction to Artificial Intelligence

Title Exercise Introduction to Artificial Intelligence

Sub-title E IAI

Semester 3


Elective Module

68

Module: Advanced Machine Learning

Module Advanced Machine Learning

Module level Master

Subtitle AML


Semester 3



Language English


Elective Module


Workload Total 180h, Lecture hours 60h, Self-study 120sh

Lecture hours 60


6


It is advised to have knowledge in Linear algebra and probability theory and statistics

Learning objectives

Advanced Machine Learning

On successful completion of this module, students should be able to recall and explain the theoretical foundations and concepts underlying Machine Learning techniques, in particular:


Students

know and understand the following topics:

Linear regression

Regularization

Support Vector Regression

Gaussian Processes

Bayesian Estimation

Probability Distributions (Gaussian, Bernoulli, Multinomial, Dirichlet, Beta), Conjugate Priors

Approximate inference: sampling techniques, MCMC

Mixture Models

Latent Factor Models (PCA, Factor Analysis, ICA)

69

Latent Dirichlet Allocation

Bayesian Non-Parametric Methods (Dirichlet Processes, Chinese Restaurant Process, Beta Processes, Indian Buffet Process)

Support Vector Machines

Structured Output Learning

Abilities / Skills

Students

derive, explain and apply the following practical machine learning methods and algorithms:

Linear regression: Least-squares regression, Ridge regression Probability density estimation: Maximum Likelihood, Maximum-A-

Posteriori, Bayesian estimation, EM algorithm for Mixture-of-Gaussians estimation

Gaussian Processes for regression Support Vector Regression Gibbs Sampling, MCMC Latent Dirichlet Allocation Dirichlet Processes Beta Processes Support Vector Machines Structured Output Regression

Competencies

Students

discuss the advantages and disadvantages of the covered machine

learning techniques

find practical solutions to complex real-world machine learning

problems

work on practical problems in a team

Content

Advanced Machine Learning

This lecture will extend the scope of the "Machine Learning" lecture with additional and, in parts, more advanced concepts. In particular, the lecture will cover the following areas:

Regression techniques (linear regression, ridge regression, support vector regression) Gaussian Processes

Bayesian Estimation Bayesian Nonparametric methods (Dirichlet Processes, Beta

Processes) Structured Output Learning

Media Lecture script in form of printed slides, Additional handouts for certain topics, Web page with supplementary material and exercises: http://www.mmp.rwth-aachen.de/teaching

Literature

C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006

T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, 2nd Edition, Springer, 2009

C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006

70


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Advanced Machine Learning

6 0 0 90

Lecture: Advanced Machine Learning

0 3 75 0

Exercise: Advanced Machine Learning

0 1 45 0

Teaching Unit / Examinations: Examination Advanced Machine Learning

Title Examination Advanced Machine Learning

Sub-title Exa AML

Semester 3


Elective Module

Teaching Unit / Examinations: Lecture Advanced Machine Learning

Title Lecture Advanced Machine Learning

Sub-title L AML

Semester 3


Elective Module

Teaching Unit / Examinations: Exercise Advanced Machine Learning

Title Exercise Advanced Machine Learning

Sub-title E AML

Semester 3


Elective odule

71

Module: Power Electronics

Module Power Electronics

Module level Master

Subtitle PE-CSA


Semester 3

Person in charge Rik W. De Doncker

Lecturer Lecture: Rik W. De Doncker

Exercise: Research Associates

Language English


Elective Module



Lecture hours 60h


5


The basic knowledge of power electronics, as they are taught in Power Electronics – Fundamentals, Topologies and Analysis, are expected.

Learning objectives

Power Electronics

Overall goal: Introduction and understanding of control, synthesis and applications of modern power electronic systems. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

understand of basic topologies for power electronic applications.

Abilities / Skills

Students

analyze the dynamic behavior of components and circuits, the control

concepts, parasitic effects and electromagnetic compatibility.

72

design an appropriate power electronic solution for each application

including hardware and control.

Competencies

Students

evaluate independently existing power electronic solutions and to

optimize them with regard to the application, e.g. for best efficiency.

Content

Power Electronics

Power Electronics generally have the goal to perform electrical energy conversion at high efficiency. The course focuses on the following aspects of converter design:

Minimum converter losses silicon and magnetics losses thermal design Soft switching of silicon devices to improve device ratings Using snubbers Soft-switching converter topologies Transformers in power electronics, using uni- and bidirectional core

excitation AC-AC converters Control of voltage source converters High-power electronics Examples


Literature Mohan, Undeland, Robins, Power Electronics, John Wiley and Sons


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Power Electronics 5 0 0 90

Lecture: Power Electronics

0 3 60 0

Exercise: Power Electronics

0 1 30 0

Teaching Unit / Examinations: Examination Power Electronics

Title Examination Power Electronics

Sub-title Exa PE

Semester 3

73


Elective Module

Teaching Unit / Examinations: Lecture Power Electronics

Title Lecture Power Electronics

Sub-title L PE

Semester 3


Elective Module

Teaching Unit / Examinations: Exercise Power Electronics

Title Exercise Power Electronics

Sub-title E PE

Semester 3


Elective Module

74

Module: Processes and Principles of Lightweight Design

Module Processes and Principles of Lightweight Design

Modul level Master

Code PPLD


Semester 3

Person in Charge Univ.-Prof. Dr.-Ing. Kai-Uwe Schröder,

Lecturer Univ.-Prof. Dr.-Ing. Kai-Uwe Schröder,

Language English

Assignment to the curriculum Elective module



Lecture hours 60h

ECTS-Credit Points (CP) 6

Requierments according to examination regulation

-none-

Learning Objectives

Processes and Principles of Lightweight Design

After successfully completing this course, the student will have acquired

the following learning outcomes:

Knowledge and Understanding:

Students

have a broad understanding of product development processes and

can structurally andindependently work on new technical tasks using

design methodology;know how to systematically analyze, evaluate

and combine partial solutions.

Abilities / Skills:

Students

75

apply basic rules and principles of Embodiment Design in order to

create optimal products for the relevant requirements, in particular

structural demands;

realize special aspects of thin-walled lightweight structures and to

design them properly;

analyze technical problems and tasks, identify existent restrictions

and hence properly elaborate technical specifications.

Competencies:

Students

know methods to design and dimension structures and are able to

explain them

check the correctness of results of numerical simulation software

Content

Processes and Principles of Lightweight Design

Introduction to design methodology and lightweight design Technical tasks: Purposes of technical systems; Methods to derive

requirements and setting up requirements lists Development of technical concepts/Conceptualization: Function

structures; Discursive, heuristic and empiric methods for problem solving

Concept evaluation: Methods to evaluate and select among variants Rules of Embodiment Design: Simple, clear and safe Principles of Embodiment Design: Principles of force transmission,

principle of the division of tasks, principle of self-help, principle of stability and bi-stability, principle of fault free design Design for X: Allow for expansion, allow for creep and relaxation, ease of assembly

Idealization of structures: Beam theory (cutting forces, deflection line, shear stresses); Composite beams

Shear flow in thin-walled closed cross sections: Location of shear center; Shear flexible beams; Torsion of thin-walled cross sections

Shear web theory: Stiffened shear web with rigid end cross sections; 2-D and 3- D stiffened shear webs

Deformation of elastic structures: Principle of virtual work; Maxwell-Mohr; Deformation of stiffened shear webs

Statically indeterminate structures: Calculation of stresses in lap joints; Load introduction problems - Stability problems in slender and thin-walled structures: Fundamentals of beam, plate and shell buckling (Influence of plasticity and imperfections); Buckling of plates; Buckling of shells (stiffened and unstiffened)


Literature Lecture Notes, Students also receive a list of relevant literature


Title

Code

ECTS

Workload Duration

of Exam )

Lecture H. (SWS)

Self-Study

Examination : Processes and

6 0 0 120

76

Principles of Lightweight Design

Lecture : Processes and Principles of Lightweight Design

0 2 60 0

Exercise (Übung): Processes and Principles of Lightweight Design

0 2 60 0

Teaching Unit / Examinations: Examination Processes and Principles of Lightweight Design

Title Examination Processes and Principles of Lightweight Design

Sub-title Exa PPLD

Semester 3

Connection to the curriculum Elective Module

Teaching Unit / Examinations: Lecture Processes and Principles of Lightweight Design

Title Lecture: Processes and Principles of Lightweight Design

Sub-title L PPLD

Semester 3


Teaching Unit / Examinations: Exercise Processes and Principles of Lightweight Design

Title Exercise: Prüfung Fundamentals of Lightweight Design

Sub-title E PPLD

Semester 3


77

Module: Applied Numerical Optimization Engineering

Module Applied Numerical Optimization Engineering

Module level Master

Subtitle ANOE


Semester 3

Person in charge Univ.-Prof. Alexander Mitsos, Ph.D.

Lecturer Univ.-Prof. Alexander Mitsos, Ph.D.

Language English


Elective Module



Lecture hours 60h


4


none

Learning objectives

Applied Numerical Optimization Engineering

Overall goal: In mechanical and chemical engineering numerical optimization methods are becoming more and more accepted. In the near future numerical optimization will be one of the standard tools of any engineer. In this lecture, the students learn to apply the basic techniques of mathematical optimization on applications from mechanical and chemical engineering. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

understand the statement of mathematical optimization problems

with objective functional, model and constraints as a basis to solve

arbitrary problems;

know which numerical solution method is to be used for the solution

of such problems;

78

understand the classification of the optimization problems and is able

to allocate arbitrary problems to the corresponding class;

understand the need for the numerical solution for arbitrary

mathematical optimization problems and are able to implement the

basic numerical concepts in their own algorithms.

Abilities / Skills

Students

master the derivation of the optimality conditions for constrained and

unconstrained problems with non-linear constraints.

Competencies

Students

will be enabled to analyse the problem statement and to produce the

concrete solution of the problem by means of the home-works

(method competence).

Content

Applied Numerical Optimization Engineering

Definition: Mathematical Optimization

Problem formulation: objective functional, model und constraints

Examples of Optimization Problems

Classification of Optimization Problems

Mathematical basics 1: continuity, differentiability

Mathematical basics 2: gradient, Hesse matrix, convexity

Optimality conditions for unconstrained problems

Solution concepts for unconstrained problems, direct, indirect numerical solution, principle of line search and trust region

Line search strategies: Armijo and Wolfe conditions

Methods for the Determination of the descent direction: Steepest Descent, Conjugate Gradients

Methods for the determination of a descent direction: Newton method

Practical Newton methods: Inexact, Modified and Quasi-Newton method

Trust Region methods: the Dogleg method

Regression problems: method of Least-Squares

Gauss-Newton solution method for regression problems

Levenberg-Marquardt solution method for regression problems

Example of an optimization problem: Ethanol extraction

Derivation of the KKT optimality conditions

Linear programming (LP)

Interior point methods for LPs

79

Simplex method for LPs

Quadratic programming (QP)

Solving the KKT system for QPs

Active-Set methods for QPs

Solution strategies for non-convex QPs

Gradient-Projection method for QPs

Interior-Point methods for QPs

Solution of general nonlinear problems (NLP)

Penalty Methods for NLPs

Log-Barrier method for NLPs

Augmented Lagrangian method for NLPs

SQP methods: Line Search SQP

Examples for optimization problems: Layer Crystalliser, Distillation Column

Introduction to mixed-integer optimization:

Branch and Bound

Outer-Approximation

Introduction to Dynamic Optimization:

Optimality conditions

Simultaneous solution methods: full discretisation

Continuous problem formulation: adjunct equations / Hamilton form

Dynamic Optimization: Sequential Solution Method

Derivation of the sensitivity equation

Examples for dynamic optimization problems

Short introduction to State Estimation

Optimization under Uncertainty

Two-Stage Stochastic Programming

Introduction to Semi-Infinite Programming (SIP)

Solution Approaches for SIP


Literature

Lecture Notes

Nocedal & Wright (2006), Numerical Optimization

Students also receive a list of further relevant literature


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

80

Examination: Applied Numerical Optimization Engineering

4 0 0 15

Lecture: Applied Numerical Optimization Engineering

0 30 30 0

Exercise: Applied Numerical Optimization Engineering

0 30 30 0

Teaching Unit / Examinations: Examination Applied Numerical Optimization Engineering

Title Examination Applied Numerical Optimization Engineering

Sub-title Exa ANOE

Semester 3


Elective Module

Teaching Unit / Examinations: Lecture Applied Numerical Optimization Engineering

Title Lecture Applied Numerical Optimization Engineering

Sub-title L ANOE

Semester 3


Elective Module

Teaching Unit / Examinations: Exercise Applied Numerical Optimization Engineering

Title Exercise Applied Numerical Optimization Engineering

Sub-title E ANOE

Semester 3


Elective Module

81

Module: Numerical Methods in Mechanical Engineering

Module Numerical Methods in Mechanical Engineering

Module level Master

Subtitle NMME


Semester 3

Person in charge Univ.-Prof. Dr.-Ing. Bernd Markert

Lecturer Univ.-Prof. Dr.-Ing. Bernd Markert

Language English


Elective Module



Lecture hours 5


7


Mandatory: none

Recommended: vector calculus, differential calculus, integral calculus

Learning objectives

Numerical Methods in Mechanical Engineering

Overall goal: Students gain the basic knowledge about the numerical methods commonly used in mechanical engineering.



Students

know he theoretical foundations of current numerical methods in

engineering;

know how to bridge between the physical formulation of a problem

and a mathematical formulation suited to numerical approximation

methods;

understand the individual steps and specific transformations required

for the construction of an approximate numerical solution.

Abilities / Skills

82

Students

apply approximation techniques and analyse the results obtained by

various approximation methods;

use their acquired knowledge to develop new approximation

methods;

critically judge the consistency and correctness of numerical

methods;

apply variational methods to obtain equivalent formulations of a

problem of differential equations.

Competencies

Students

construct basis functions compatible with the boundary conditions;

construct and apply a variety of approximation methods based on the

WRM (collocation by points, collocation by subdomains, Galerkin's

method, least squares method, Ritz method);

solve constrained optimization problems by using the Lagrange

Multipliers Method;

construct the associated energy potential and to apply the

stationarity principle for a conservative mechanical problem;

apply tools of numerical integration.

Content

Numerical Methods in Mechanical Engineering

From intuitional perception to the mathematical formulation of engineering problems; examples. Choice of assumptions and mathematical tools to formulate problems

Classes of solution methods (overview): Analytical solutions, approximate solutions, direct approximation, approximate solution after transformation of the problem

Classes of physical problems: discrete systems, continuous systems. Equilibrium, eigenvalue, and propagation problems

Integral forms. Weak formulation of problems. The Method of Weighted Residuals (WRM)

Introduction to variational calculus. Functionals

Functionals associated with an integral form

The stationarity principle. Stationarity conditions. Examples from mechanics

The method of Lagrange multipliers. Mixed and complementary formulations

Catalogue of functionals used in continuum mechanics and their specific features

Discretisation of integral forms. Collocation by points. Collocation by subdomains

Galerkin’s method. Least Squares Method. Examples

Ritz' method. Examples

Numerical integration. Newton-Cotes method. Gauss method. Examples

83

The Finite Element Method. Shape functions, construction of finite elements

Matrix representation in the FEM. Stiffness matrix. Boundary conditions

Examples from structural engineering. Software packages in engineering

Media e-Learning L²P, computer lab

Literature Lecture Notes (online)



Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Numerical Methods in Mechanical Engineering

7 0 0 120

Lecture: Numerical Methods in Mechanical Engineering

0 3 105 0

Exercise: Numerical Methods in Mechanical Engineering

0 2 30 0

Teaching Unit / Examinations: Examination Numerical Methods in Mechanical Engineering

Title Examination Numerical Methods in Mechanical Engineering

Sub-title Exa NMME

Semester 3


Elective Module

Teaching Unit / Examinations: Lecture Numerical Methods in Mechanical Engineering

Title Lecture Numerical Methods in Mechanical Engineering

Sub-title L NMME

Semester 3


Elective Module

84

Teaching Unit / Examinations: Exercise Numerical Methods in Mechanical Engineering

Title Exercise Numerical Methods in Mechanical Engineering

Sub-title E NMME

Semester 3


Elective Module

85

Module: Strategic Technology Management

Module Strategic Technology Management

Module level Master

Subtitle STM


Semester 3

Person in charge Univ.-Prof. Torsten-Oliver Salge, Ph. D.

Lecturer Univ.-Prof. Torsten-Oliver Salge, Ph. D.

Language English


Elective Module

Teaching form

The course grade will be determined based on one of the following modes of

evaluation:

(a) Colloquium and presentation (50%) and written exam (50%,

duration: 60 minutes); or

(b) Colloquium and presenation (50%) and written (individual) term

paper (50%);

or

(c) Written exam (100%, duration: 60 minutes)

The final mode of evaluation (A, B, or C) will be announced and publicly displayed prior to the first class session. In general, grading for this class will be based on mode (a).


Lecture hours 60h


5


-none-

Learning objectives

Strategic Technology Management

Overall goal: Students gain theoretical and practical knowledge in technology

and innovation management as preparation for interdisciplinary leadership

roles in research and development (R&D) and beyond.

After successfully completing this course, the student will have acquired the

following learning outcomes:

86


Students

understand and critically reflect upon key concepts and theories in

strategic TIM;

understand and critically discuss conceptual and empirical research

papers on strategic TIM.

Abilities / Skills:

Students

analyze and develop adequate solutions to some of the practical

challenges of strategic TIM;

apply important tools in strategic TIM intelligently based on a thorough

understanding of their respective strengths and weaknesses.

Content

Strategic Technology Management

This course provides a case- and/or research-based introduction to strategic technology and innovation management (TIM)

This involves revisiting some of the foundational concepts and debates in strategic management and examining key strategic decisions at the heart of technology and innovation management

These might pertain for instance to

the selection of technology fields,

the composition of innovation portfolios,

the timing of technology development initiatives,

the development of innovation processes,

the search for new ideas,

the involvement of users,

the implementation of modular designs,

the orchestration of strategic alliances,

the protection of intellectual property.

As part of this course, participants will have the opportunity to become familiar with case studies and/or research papers related to these topics.

The course is typically composed of six longer classroom sessions comprising a mixture of traditional lectures, case/paper discussions and student presentations.

Please note, that a detailed course outline and reading list will be made available ahead of the first session

87





Title Code ECTS

Workload (SWS / h)

Duration of Exam (min) Lecture h.

(SWS) Self-Study (h)

Examination: Strategic Technology Management

5 0 0 See examination options

Lecture: Strategic Technology Management

0 2 45 0

Exercise: Strategic Technology Management

0 2 45 0

Teaching Unit / Examinations: Examination Strategic Technology Management

Title Examination Strategic Technology Management

Sub-title Exa STM

Semester 3


Elective Module

Teaching Unit / Examinations: Lecture Strategic Technology Management

Title Lecture Strategic Technology Management

Sub-title L STM

Semester 3


Elective Module

Teaching Unit / Examinations: Exercise Strategic Technology Management

Title Exercise Strategic Technology Management

Sub-title E STM

Semester 3


Elective Module

88

89

Module: Finite Element Methods for Engineers

Module Finite Element Methods for Engineers

Module level Master

Subtitle FEM


Semester 3

Person in charge Univ.-Prof. Dr.-Ing. Mikhail Itskov

Lecturer Univ.-Prof. Dr.-Ing. Mikhail Itskov

Language English


Elective Module



Lecture hours 60h


5


none

Learning objectives

Finite Element Methods for Engineers

Overall goal: Students gain the basic knowledge about finite element methods and their application to solid and structural mechanics. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

understand why the FE-Method and the other numerical methods

behind are important for engineering practice;

are familiar with the fundamental concepts of variation calculus;

know the basic concept of FEM.

Abilities / Skills

Students

90

use the finite element method for plane strain, plane stress and torsion problems.

Competencies

Students

find solutions for truss systems under various boundary conditions;

find solutions for mechanical problems by using weighted residual

methods.

Content

Finite Element Methods for Engineers

General introduction, concept of the finite element method

Symbolic assembly procedure

Assembly procedure

Global and local coordinates

Stiffness matrix for trusses / coordinate

transformation

Variation techniques

Solution of truss structures.

Variation techniques, Euler-Lagrange equation

Natural and forced boundary conditions

Multiple integrals, Gauss-Theorem

Variations of elementary algebraic functions

Variation principle for linear self-ad joint diff. operators

Solution of some classical variation problems

Principle of virtual work as a weak form of the momentum balance, variation principles of mechanics (Lagrange, Hu-Washizu)

Differential equation of a linear elastic bar, analytic solution for various load cases

Rayleigh-Ritz method, weighted residual approximations,

Point or subdomain collocation

Galerkin method, least-squares method, linear elastic bar approximated by a continuous shape function

Displacement formulation

Three-field (mixed) formulation

Examples to weighted residual approximations

Requirements to shape functions

Continuous shape functions, piecewise defined

shape functions, approximation by piecewise defined

Shape functions.

91

Two-dimensional problems of elasticity, triangular element, plain strain and plane stress problems

Torsion of a prismatical bar

Examples for plain strain and plane stress problems discretized by linear triangular elements

Axisymmetric stress analysis, 3-D stress analysis

Repetitorium





Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Finite Element Methods for Engineers

5 0 0 120

Lecture: Finite Element Methods for Engineers

0 2 45 0

Exercise: Finite Element Methods for Engineers

0 2 45 0

Teaching Unit / Examinations: Examination Finite Element Methods for Engineers

Title Examination Finite Element Methods for Engineers

Sub-title Exam FEM

Semester 3


Elective Module

Teaching Unit / Examinations: Lecture Finite Element Methods for Engineers

Title Lecture Finite Element Methods for Engineers

Sub-title L FEM

Semester 3


Elective Module

Teaching Unit / Examinations: Exercise Finite Element Methods for Engineers

Title Exercise Finite Element Methods for Engineers

92

Sub-title E FEM

Semester 3


Elective Module

93

Module: Mechatronics and Control Techniques for Production Plants

Module Mechatronics and Control Techniques for Production Plants

Module level Master

Subtitle MCTPP


Semester 3

Person in charge Univ.-Prof. Dr.-Ing. Christian Brecher

Lecturer Univ.-Prof. Dr.-Ing. Christian Brecher

Language English


Elective Module



Lecture hours 60h


6


none

Learning objectives

Mechatronics and Control Techniques for Production Plants

Overall goal: After this course, the students are able to understand the structure of mechatronic systems in the area of application of the means of production in its complexity and its context and overarching concepts of machine control systems to classify. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

know the construction and design of mechatronic systems for

production equipment;

know the characteristics of logical and mechanical numerical motion

controls of machines;

know special features of the behaviour and the modelling of

mechatronic components, especially for measuring and gripping

technology;

94

know concepts of machine control in various development systems,

as well as machine and process monitoring;

know fields of application, possibilities of an industrial engineering

system and the design.

Abilities / Skills

Students

explain application areas and display the characteristics of motion

controls required in machine and process monitoring. In addition, you

can theoretically explain the design of an application-oriented

problem and apply it to application-relevant questions.

Competencies

Students

analyze theory-based mechatronic systems for production systems

and industrial monitoring solutions and to evaluate their quality in the

industrial environment. With this competence, they are able to use

their own creative ideas and within the framework of the concepts

known to you to develop solutions and to establish the set-up of

concepts;

create control programs in various development systems and

evaluate their quality.

Content

Mechatronics and Control Techniques for Production Plants

• Introduction of Mechatronics and Control Techniques for Production Plants

• Mechanical controls

- Machine elements of mechanical controls

- Application examples for mechanical controls

• Information processing in mechatronic systems

- Theory and examples of embedded systems

- Programming of embedded systems and logical controls

• Programmable Logic Control (PLC) and Motion Control (MC)

- Programming of programmable logic controllers

- Test methods of programmable logic controllers (HIL)

• Numerical Control 1: Design, programming, CAM

- NC programming procedures (manual and workshop-oriented)

- NC programming of CAM systems

• Numerical Control 2: Interpolation

- Structure of an NC control

- Tool offset, kinematic transformation and compensations, speed control and Interpolation

• Position control of feed drives

95

- Control concept of a machine axis

- Accuracy and synchronous control of multi-axis systems

• Measurement Systems and Sensors

- Measured variables in production systems

- Position, current acceleration, force, torque, temperature and angle measuring systems

• Signal processing, process and condition monitoring

- Tasks of the process and condition monitoring

- Use of sensors and processing of sensor signals

• Robots and handling systems, Robot Control (RC)

- Areas of application

- Construction and kinematics

• Gripper technology

- Gripping principles

• Mechatronic and system-oriented engineering

- Design and simulation software (drive design and behavior modeling)

- Virtual Commissioning

Media L2P

Literature For the english lecture and exercise a set of slides with english comments can be found in the L²P of the lecture.


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Mechatronics and Control Techniques for Production Plants

6 0 0 120

Lecture: Finite Element Methods for Engineers

0 2 60 0

Exercise: Mechatronics and Control Techniques for Production Plants

0 2 60 0

Teaching Unit / Examinations: Examination Mechatronics and Control Techniques for Production Plants

Title Examination Mechatronics and Control Techniques for Production Plants

96

Sub-title Exa MCTPP

Semester 3


Elective Module

Teaching Unit / Examinations: Lecture Mechatronics and Control Techniques for Production Plants

Title Lecture Mechatronics and Control Techniques for Production Plants

Sub-title L MCTPP

Semester 3


Elective Module

Teaching Unit / Examinations: Exercise Mechatronics and Control Techniques for Production Plants

Title Exercise Mechatronics and Control Techniques for Production Plants

Sub-title E MCTPP

Semester 3


Elective Module

97

Module: Advanced Control System

Module Advanced Control System

Module level Master

Subtitle ACS


Semester 3

Person in charge Dr.-Ing. Berno J.E. Misgeld, M.Sc.

Lecturer Dr.-Ing. Berno J.E. Misgeld, M.Sc.

Language English


Elective Module


Workload Total 120h, Lecture hours 45h , Self-study 75h

Lecture hours 45


4


Course work (30%) and oral examination (70%). The final grade is calculated from coursework and oral examination achievement. Modalities of the examination will be discussed with students at the first lecture.

Learning objectives

Advanced Control System

Overall goal: Students develop an advanced understanding of multivariable system analysis and apply modern robust control techniques. This includes the application of for Students understand and apply state-space, as well as frequency domains methods, for multivariable systems. After successfully completing this course, the students will have acquired the following learning outcomes:


Students

understand and apply modern multivariable analysis and control

synthesis for complex processes in order to design feedback

controllers;

are familiar with uncertainties modelling for dynamic processes and

multiple and opposed design goals for controller synthesis;

98

understand and apply state-space, as well as frequency domains

methods, for multivariable systems.

Abilities / Skills

Students

use modern time and frequency domain methods for analysis and

design of controllers for multivariable plants.

Competencies

Students

analysing linear multivariable processes and synthetise optimal

robust controllers.

Content

Advanced Control System

• Fundamentals of multivariable systems and representation • Analysis of multivariable systems, modelling of uncertainties • General control configuration, performance and robustness • H2- (LQR/LQG) control • Introduction to robust H∞-control • Implementation aspects of robust controllers • mu-Synthesis

Media e-Learning L2P, Power Point, Script

Literature

• S. Skogestad und I. Postlethwaite, Multivariable Feedback Control, Wiley, 2005 • M. Morari und E. Zafiriou, Robust Process Control, Prentice-Hall International, 1989 • B. A. Francis, A Course in Hinf-Control Theory, Springer-Verlag, Berlin, 1987

• R. A. Hyde, Hinf-Aerospace Control Design, Prentice-Hall International, 1989


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Advanced Control System

4 0 0 30 (oral exam)

Lecture: Advanced Control System

0 2 40 0

Exercise: Advanced Control System

0 1 35 0

Teaching Unit / Examinations: Examination Advanced Control System

99

Title Examination Advanced Control Systems

Sub-title Exa ACS

Semester 3


Elective Module

Teaching Unit / Examinations: Lecture Advanced Control System

Title Lecture Advanced Control System

Sub-title L ACS

Semester 3


Elective Module

Teaching Unit / Examinations: Exercise Advanced Control System

Title Exercise Advanced Control System

Sub-title E ACS

Semester 3


Elective Module

100

Elective Courses – Fourth Term

Module: Internship (Industrial Track)

Module Internship

Module level Master

Subtitle I


Semester 4

Person in charge Univ.-Prof. Dr. rer. pol. Burkhardt Corves

Lecturer Univ.-Prof. Dr. rer. pol. Burkhardt Corves

Language English


Elective module

Teaching form Internship report (Examination), Internship

Workload Total 12 weeks


10


none

Learning objectives See guidelines for practical training in the examination regulations

Content See guidelines for practical training in the examination regulations

Media -

Literature -


Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Internship

10 0 0 -

Internship: Internship

0 0 300 0

101

Teaching Unit / Examinations: Examination Internship

Title Examination Internship

Sub-title Exa I

Semester 4


Elective module

102

Module: Research Project (Academic Track)

Module Research Project

Module level Master

Subtitle RP


Semester 4

Person in charge Univ.-Prof. Dr. rer. pol. Burkhardt Corves

Lecturer Univ.-Prof. Dr. rer. pol. Burkhardt Corves

Language English


Elective module

Teaching form Research Project report (Examination) (Exa)

Workload Total 12 weeks

Lecture hours -


10


none

Learning objectives

Research Project



Students

know how to plan a research project;

know how to design experiments to get a maximum output (effects

and interactions of the parameters on the result);

know how to evaluate the results using advanced statistical methods.

Abilities / Skills

Students

work independently on a scientific topic.

103

Competencies

Students

analyse the problem at-hand;

determine possible ways to solve it, explain the best way to do so by

comparing and assessing the given possibilities;

apply the chosen way to solve the problem;

document every step of the project;

present their results oral and written.

Content

Research Project

Basics of project management

Basics of time planning

Planning experiments

Evaluation of results with statistical methods

Oral and written presentation of results

Actual project work on a subject chosen by student from a wide range of topics offered at IMGR





Title Code ECTS


Lecture h. (SWS)

Self-Study (h)

Examination: Research Project

10 0 0 -

Lecture/Exercise: Research Project

0 0 300 0

Teaching Unit / Examinations: Examination Research Project

Title Examination Research Project

Sub-title Exa RP

Semester 4


Elective module

104

Compulsory Course – Fouth Term

Module: Master Thesis

Module Master Thesis

Module level Master

Subtitle MaTh


Semester 4

Person in charge RWTH Aachen

Lecturer RWTH Aachen

Language English


Compulsory Module

Teaching form Supervision and assistance by the relevant professor

Workload 6 Months

Lecture hours -


20


The topic of the Master thesis cannot be assigned until 80 CPs have been achieved. Reasonable exceptions are governed by the Board of Examiners upon request by the candidate. The Master´s defense colloquium must be held four weeks after the written completion of the Master´s Thesis.

Learning objectives The students learn the independent approach and processing of academic themes, their documentation and written interpretation within a set deadline. They acquire systematic academic research skills.

Content Completed academic paper which shall show that the students are capable of independently processing a problem related to their subject according to academic methods within a set deadline.

Media -

Literature According to the relevant research questions of the Master Thesis


Title Code ECTS Workload (SWS / h)

105

Lecture h. (SWS)

Self-study (h) Duration of Exam (min)

Master Thesis 20 0 0 0

Master Defense colloquium

0 0 0 15-60

Teaching Unit / Examinations: Examination Master Thesis

Title Master Thesis

Sub-title Exam MaThe

Semester 4

Documents

Module Handbook Master of Science in Robotic …...1 Table of Contents Compulsory Courses – First Term.....3 Module: Robotic Systems 3 Module: Advanced Robotic Kinematics and Dynamics