Digitization of Materials - Interaktive Studienpläne der TU Ilmenau

Elaboration of a given topic and presenting the topic in the excercises, discussing the topic with the audience

Prof. Dr. Peter Schaaf und Mitarbeiter

Dieses Modul enthält mindestens eine alternative semesterbegleitende Abschlussleistung. Bitte beachten Sie, dass diese in der Regel schon zu Beginn des Semesters, in dem diese angeboten wird, angemeldet werden muss.
Über die Details und Zeiträume dazu werden Sie vom Lehrenden und/oder dem Prüfungsamt informiert. Fragen Sie gegebenenfalls unbedingt beim Lehrenden nach.

This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered.
The lecturer and/or the examination office will inform you about the details and time periods. If necessary, be sure to ask the lecturer.

basic knowledge of materials science and engineering, knowledge in methods or materials analysis and characterization, basic knowledge in informatics and mathematics.

After the course the students can name the various ways and formalisms of digital materials representations.

The students are able to work with digital representations of materials, they can derive digital representations of materials from material analytical measurements. They can judge and correlate digital representations with properties and applications.

Students can use and apply various computer programs for digitalization of materials.

Students can use programming and software tool for working with digital materials data.

Students can use different methods for selecting and evaluating materials data using databases/Asby diagrams.

Students evaluate materials according to their intended use and the fulfilment of the requirement profile.
Students have learned and can apply methods for determining material properties and recording material properties. The special features of the use of coatings have been worked out increasingly. The students can evaluate material parameters coherently on the property values of materials.

The students can statistically evaluate sample series using Weibull distribution methods and similar distributions.

Students can create, apply and evaluate optimized experimental designs. They can synthesize complex property relationships of materials from their experiments.

Students are able to present and discuss a scientific topic in an scientific audience (excercises).

After intensive discussions and group work during the exercises, the students can correctly assess and appreciate the achievements of their fellow students. They take criticism, heed remarks and accept suggestions.

Factual Competences:

Databases of Materials properties, materials selection

Methods or digitization of materials,

multiscale- and multidimensional data for materials,

methods of digital parametrizations for materials.

Transformation of material analysis data into digital representations.

1. objective: microstructure-properties - the most important material relationship
2. requirements for materials - for power engineering - for automotive engineering - for microelectronics - for nanotechnology - for chemical industry - for biomaterials
3. comparability of material properties - material databases - Asby diagrams
4. methods of selection
5. evaluation methods

The lecture will be accompanied by an exercise, partly using simulations, computational materials science, database work and Ashby diagrams.

1. material characterization methods for obtaining data material, such as classical material testing methods applied to thin films; X-ray diffraction, X-ray fluorescence, atomic force microscopy, electron microscopy, analytical electron microscopy, eye-spectroscopy
2. material description: description of selected material properties by mathematical models
3. optimized design of experiments
4. Creation of optimized test plans for the analysis of material and component properties
5. mathematical distribution functions for evaluation of experiments with few samples, Weibull distributions, Weibull nets

Methodological Competences

Students can analyse a digital representation of materials and draw conclusions. They are able to convert measurements of properties to a digital representation of materials.

Self-reflecting competences

Students know how to deal with digital representations and can judge about deficiencies and limitations. They know how to extend the problem and find a solution.

Social Competences

After the seminar, the students have gain ed deeper knowledge for selected examples, and they have learned how to search information and how to present this in a talk and to discuss the problems. After intensive discussions and group work during the exercises, the students can correctly assess and appreciate the achievements of their fellow students. They take criticism, heed remarks and accept suggestions.

The lecture will be accompanied by an exercise, partly using simulation software and examples.

powerpoint and blackboard lecture, animations, examples, excercises, scriptum, computer work, software packages, specialized literature.

moodle course: https://moodle.tu-ilmenau.de/course/view.php?id=2695

Enrollment starts at March 1st. The enrollment key: please ask via e-mail.

A list of recommended literature will be handed at the beginning of the course via moodle.

Speacialized material will be provided at the beginning via moodle.