Offers

PostDoc

If you are interested to join as a PostDoc, please send a brief letter of motivation to the respective contact person.

 

Postdoctoral Researcher – Acoustic Emission Analysis

For the Researchgroup Condition Monitoring at the Chair "Hybrid Materials" with the Professorship "Mechanical Engineering" at the University of Augsburg, we are looking for a Scientific Associate (m/f/d) - Post-Doc with a focus on Acoustic Emission Analysis.

 

Our research mainly involves the use of sensor data for process monitoring and structural health monitoring. An important method for this is acoustic emission analysis, which should fall under your responsibility. Your research should enable the development of new concepts for the manufacturing, approval, and monitoring of pressure vessels in the field of hydrogen driven mobility. This gives you the opportunity to contribute to achieving climate and sustainability goals. Furthermore, you have the opportunity to independently explore new research fields and topics.

 

If you are interested in this position, please contact us by phone or email or send your application directly to the contact persons.

Group leader "Condition Monitoring"
Mechanical Engineering
Director AI production network
Mechanical Engineering

Doctoral Theses

If you are interested to join as a doctoral candidate, please send a brief letter of motivation to the respective contact person

 

Doctoral Researcher (m/f/d) – Model-based non-destructive testing of foundry cores

We are looking for a Doctoral Researcher (m/f/d) – Model-based non-destructive testing of foundry cores to join the team at the assistant Professorship Data-driven Materials processing at the University of Augsburg.


Sand cores are used in foundry technology to form non-removable geometries. In large-scale production, cores are increasingly produced with innovative inorganic substances due to lower environmentally harmful emissions. At present, however, there is still no process suitable for series production to detect and localize defects in the cores.


Your research mainly involves combining virtual models and experimental data. To this end, you will set up a test stand for non-destructive testing of the sand cores. With the help of a simulation model, the measurement data can be evaluated and defects in the core can be localized.


If you are interested in joining our team, please contact us by phone or email.

 

 

Assistant Professor
Data-driven Materials Processing

Doctoral Researcher (m/f/d) – Process monitoring for intelligent manufacturing processes

We are looking for a Doctoral Researcher (m/f/d) – Process monitoring for intelligent manufacturing processes to join the team of Mechanical Engineering in the research group Condition Monitoring at the University of Augsburg.


Your research mainly involves the use of sensor data for process monitoring of a complex production chain. The focus of your work is mainly on monitoring innovative injection molding processes. Therefore, you will use various sensor technologies for example acoustic sensors or transducers. In addition to the sensory data acquisition, you will also ensure that the data is further processed in the diagnosis and prognosis systems developed by you, including AI-based systems. Your research will not remain a theoretical concept, but will be implemented directly in the university's own research factory of the AI Production Network or within the framework of an interdisciplinary Bavarian research network. In this way, you can make your contribution to digitalization and resource efficiency in production.

 

If you are interested in this position, please contact us by phone or email or send your application directly to the contact persons.

 

 

Group leader "Condition Monitoring"
Mechanical Engineering
Director AI production network
Mechanical Engineering

Doctoral Researcher (m/f/d) – Robot-based multimodal non-destructive testing

We are looking for a Doctoral Researcher (m/f/d) – Robot-based multimodal non-destructive testing to join the team of Mechanical Engineering in the research group Condition Monitoring at the University of Augsburg.


Non-destructive testing is an essential part of the manufacturing process, particularly for safety-critical and highly stressed structural components in industries such as aerospace. Various testing concepts, ranging from visual inspection to X-ray tomography, are employed based on specific requirements. In your research, you will play a leading role in the design and implementation of a robot-based testing cell. Your main focus will be on exploring how data acquisition across different testing methods can be automated for individual inspection tasks using industrial robots and your developed algorithms. To enhance the quality of testing, you will investigate concepts for selecting testing procedures and fusing measurement results. Artificial intelligence methods will be applied for data analysis. The findings from the testing cell will contribute to evaluating the quality of other manufacturing processes and labeling datasets from production. Therefore, you will be responsible for a central facility within the research factory of the AI Production Network at the University of Augsburg.

 

If you are interested in this position, please contact us by phone or email or send your application directly to the contact persons.

 

 

Group leader "Condition Monitoring"
Mechanical Engineering
Director AI production network
Mechanical Engineering

Master Theses

If you are interested, please contact the respective research associate by e-mail

 

Localisation of defects in foundry cores using modal analysis

In this thesis a virtual model is used to investigate how defects in foundry cores can be characterised and localised based on a modal analysis. For this purpose, a parametric finite element simulation model is created that  can model various defects. An analysis of the resulting natural frequencies and shapes will be used to localise defects.

 

More information can be found in the hyperlinked pdf:

Localisation of defects in foundry cores using modal analysis

 

 

 

Assistant Professor
Data-driven Materials Processing

Development of a physically-informed neural network for the optimisation of component geometry during tube bending

This work aims at a fast computational process model of the freeform bending process: A physics-informed neural network which is trained with experimental data of bending constant radii and utilizes additional physical  bending knowledge by integrating Timoshenko's beam theory. The model is able to predict the resulting plastic deformation of the tube after exiting the die and is fast enough for inverse optimization of the bent components  geometry.

 

More information can be found in the hyperlinked pdf:

Development of a physically-informed neural network for the optimisation of component geometry during tube bending

 

 

Assistant Professor
Data-driven Materials Processing

Development and testing of sensors based on dielectric analysis for the application of non-destructive material characterization

The aim of the work is to further develop the method of dielectric analysis with regard to sensor design, measurement and evaluation in such a way that it is possible to obtain quantitative information about the material properties of the sample independently of external influences such as sample geometry or measuring distance. After a successful development, the sensor is supposed to be used in a larger measurement setup for non-destructive material characterization.

 

More information can be found in the hyperlinked pdf:

Development and testing of sensors based on dielectric analysis for the application of non-destructive material characterization

 

 

PostDoc
Mechanical Engineering

Development and testing of sensor technology based on active thermography for use as a non-destructive method for determining thermal material properties

The aim of the work is to further develop a novel variant of active thermography for use in determining characteristic values. This should make it possible to infer quantitative properties of the material by heating the sample locally and following the heat propagation on its surface. Numerical simulation methods can also be used for this purpose. This should make it possible to investigate thermal properties of materials non-destructively and without great effort.

 

More information can be found in the hyperlinked pdf:

Development and testing of sensor technology based on active thermography for use as a non-destructive method for determining thermal material properties

 

 

PostDoc
Mechanical Engineering

Development and testing of sensors based on eddy current for the application of non-destructive material characterization

The aim of the work is to further develop the method of eddy current testing with regard to sensor design, measurement and evaluation in such a way that it is possible to obtain quantitative information about the material properties of the sample independently of external influences such as sample geometry or measuring distance. After a successful development, the sensor is supposed to be used in a larger measurement setup for non-destructive material characterization.

 

More information can be found in the hyperlinked pdf:

Development and testing of sensors based on eddy current for the application of non-destructive material characterization

 

 

PostDoc
Mechanical Engineering

Designing and developing a smart sensor for process and condition monitoring

For real-time monitoring, which is a central topic in the working group “condition monitoring” and is already being implemented through the use of ultrasonic sensor, a smart sensor is now to be developed. For this purpose, this work aims to form a basis by enhancing a commercially available sensor of lower frequency with a microcontroller / single-board computer in such way, that it is able to collect, process and forward the data to a central computer via a suitable communication interface. The comparison of the developed sensor with a corresponding commercial system should round off the work.

 

More information can be found in the hyperlinked pdf:

Designing and developing a smart sensor for process and condition monitoring

 

 

PhD student
Mechanical Engineering

Bachelor Theses

If you are interested, please contact the respective research associate by e-mail

 

 

 

Additive manufacturing of fiber-reinforced high-performance metal composites

The aim of this work is to investigate the introduction of fibers into the manufacturing process of 3D-printed copper components. The manufacturing route of "fused deposition modeling“ (a copper particle-filled plastic  filament) is to be used to produce the structure and insert continuous fiber bundles into the component. In a subsequent washing, pyrolysis and sintering process, a fiber-reinforced copper composite material can ultimately  be produced from the green body.

 

More information can be found in the hyperlinked pdf:

Additive manufacturing of fiber-reinforced high-performance metal composites

 

 

Group leader "Processes"
Hybrid Composite Materials

Localisation of defects in foundry cores using modal analysis

In this thesis a virtual model is used to investigate how defects in foundry cores can be characterised and localised based on a modal analysis. For this purpose, a parametric finite element simulation model is created that  can model various defects. An analysis of the resulting natural frequencies and shapes will be used to localise defects.

 

More information can be found in the hyperlinked pdf:

Localisation of defects in foundry cores using modal analysis

 

 

 

Assistant Professor
Data-driven Materials Processing

Development of a physically-informed neural network for the optimisation of component geometry during tube bending

This work aims at a fast computational process model of the freeform bending process: A physics-informed neural network which is trained with experimental data of bending constant radii and utilizes additional physical  bending knowledge by integrating Timoshenko's beam theory. The model is able to predict the resulting plastic deformation of the tube after exiting the die and is fast enough for inverse optimization of the bent components  geometry.

 

More information can be found in the hyperlinked pdf:

Development of a physically-informed neural network for the optimisation of component geometry during tube bending

 

 

Assistant Professor
Data-driven Materials Processing

Working in the lab

We regularly offer opportunities to work in the laboratory (HiWi jobs). If you are interested in working with us, please send a short email with a letter of motivation directly to the respective scientific assistant.

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