Theses
Leibniz-IWT offers an extensive selection of topics for Bachelor’s, Master’s, and project theses, all closely aligned with the institute’s core research areas. These opportunities encompass both experimental and theoretical work, providing flexibility for students to engage individually or collaboratively in groups. Additionally, unsolicited applications on specialized topics are always welcome.
The full list of available Bachelor’s, Master’s, and project thesis topics can also be accessed through Stud.IP.
Department of Materials Engineering
General Call for Student Theses in WT-OFT
General Call for Student Theses on Lightweight Materials
General Call for Student Theses in Metallography
General Call for Student Theses in MPA Metallic Materials
General Call for Student Theses – Physical Analytics, April 2021
Department of Manufacturing Technologies
Thesis - Depth Influence in External Cylindrical Grinding
Thesis - Automation of Data Flow in the Field of Digital Manufacturing
Thesis - Characterization of the Topography of Coarse-Grained Diamond Grinding Wheels
Thesis - Diamond Machining of Single-Crystalline Materials for Optical Components
Thesis - Influence of Temperature on the Production of IOLs
Thesis - Artificial Intelligence for Predicting Topography and Subsurface Properties
Thesis - Model-Based Representation of Multi-Point Contacts in Deep Rolling
Thesis - Depth Influence in Surface Grinding
Thesis - Digital Manufacturing
Thesis - n-Process Treatment of Additively Manufactured LPBF-LB/M Samples via Deep Rolling
Thesis - Artificial Intelligence in Manufacturing
Thesis - Neural Networks for Predicting Workpiece Modifications in Deep Rolling
Thesis - Subsurface Modification of Workpieces via Deep Rolling
Call for Proposals: Mechanical Surface Treatments – Shot Peening
Call for Proposals: Mechanical Surface Treatments – Deep Rolling
Thesis - AI for Process Evaluation in Manufacturing Technology
Thesis - Strategies for Distortion Compensation through Deep Rolling
Thesis - Comparison of Modeling Approaches for Predicting Material Stress and Modification