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Hello All,
We are a group of two highly motivated students who are looking for group members for this ADP in Additive Manufacturing.
Although focused on medical implants, this project develops core engineering skills directly applicable to aerospace and mechanical engineering, including additive manufacturing (PBF-LB/M), process optimization, data-driven modeling, and surface engineering. Participants learn to link process parameters to material behavior and performance, a key capability in designing high-performance components across industries. The work on graded and locally controlled properties reflects advanced applications such as lightweight structures and thermal systems. Programming in MATLAB and Python will be carried out as part of this ADP.
Companies like Airbus use additive manufacturing to produce lightweight, high-performance aircraft components, enabling complex designs and improved fuel efficiency. Siemens Energy applies AM for turbine components, repair, and on-demand spare parts, improving performance and reducing downtime. Both rely on controlling process–structure–property relationships, the same core principles developed in this project.
The ADP description is as follows:
Powder bed fusion using a laser beam (PBF-LB/M) offers high potential for the manufacturing of complex component geometries. A growing field of application is the production of patient-specific implants. The surface of the implants plays a decisive role and can improve the ingrowth behavior. In particular, the high design freedom, as well as the possibility to locally adjust the additive manufacturing process, make it possible to biomimetically reproduce surfaces known from nature.
In this project, methods for the generation of graded surfaces for the biomimetic manufacturing of implants are to be developed. For this purpose, sample specimens are to be produced on a PBF-LB/M system using a titanium alloy and existing software for the modification of process parameters, and their surface properties are to be optically measured. From the experimentally determined relationships between process parameters and surface properties, models are to be derived. The application of these models subsequently enables a function-oriented generation of graded surface properties.
The tasks can be structured as follows:
- Familiarization with the topic and literature research
- Conducting experiments on a PBF-LB/M system for the local adjustment of process parameters (scan strategy, scan speed, laser power, etc.)
- Analysis of process monitoring data and correlation with surface properties
- Evaluation of experiments using an optical microscope (surface measurements, metallographic investigations)
- Extension of existing software for the local adjustment of process parameters
- Evaluation and documentation of the results