Bachelor’s thesis, master’s thesis or project work
© IKVTopic of the work:
Modern large-volume 3D printing systems reach a physical limit: the extruder cannot follow changes in the print path immediately, the melt “lags behind”. This results in dimensional deviations at corners and curves, which directly affect the component quality. This inertial behavior is not a fixed problem, but depends on the material, the printing speed and the temperature; a systematic, quantitative recording has not yet been carried out.
The aim of this work is to measure this behavior and to describe it using an empirical model that maps the characteristic delay time of the extruder as a function of the main process parameters. This forms the basis for a software tool that corrects the printing process in advance – even before the first layer is printed.
To do this, you start with targeted pressure tests (Design of Experiments, DoE), in which you record and measure the start-up behavior of the extruder under various conditions. From this data, you derive a model that describes how quickly or slowly the extruder reacts under which conditions. Finally, you use printed test components to demonstrate that your model measurably and reproducibly improves the print quality.
The work is related to this research project:
The work takes place in the Additive Manufacturing working group and is directly embedded in the Cluster of Excellence “Internet of Production” (IoP). At the IoP, we are researching how the production of the future can be made more flexible and efficient through the consistent networking of data and the use of machine learning. We are working on the procedural optimization of 3D printing in order to develop the process from “handicraft” to robust industrial production. The focus is on machine and material behavior as well as process control.
Objective:
The aim of this work is the experimental determination of the speed- and temperature-dependent extruder time constant in large-volume additive manufacturing as well as the integration of the derived compensation model into a predictive G-code postprocessor. The aim is to minimize over- and under-extrusion at corners, curves and infill transitions caused by inertia and significantly improve the dimensional accuracy of large-volume 3D printed components.
Your task:
| For a Bachelor's thesis, you will work on the following tasks | For a Master's thesis, you will work on the following tasks |
| Practical introduction: You start directly at the machine and produce the first test structures to geometrically record the deceleration behavior (the system inertia) of the extruder. | All points of the Bachelor thesis, plus: |
| Hardware & sensor technology: You set up a digital measuring chain and synchronously record the melt pressure, the motor torque and the material throughput during the printing tests | mathematical modeling: From the synchronously recorded sensor data (melt pressure, torque), you derive a robust empirical model that describes the systemic inertia of the extruder. |
| Design of Experiments (DoE): You run a fully automated, statistically validated test plan. You systematically vary key parameters such as speed and temperature. | You integrate your empirical model into a G-code postprocessor and program a predictive "look-ahead" function (sentence preview). |
| Data analysis & modeling: You will derive empirical models from your acquired DoE data in order to mathematically describe the dynamic start-up behavior of the extruder. | Complex validation: You leave the simple test level and validate your algorithm on demanding 3D geometries (curves, sharp corners, complex transitions, infill). |
| Validation on the component: You validate your findings by printing real corner test components and evaluating their dimensional accuracy. | Infill as an enabler: assessing the potential of compensation for industrial infill structures |
Your profile:
- Technical or scientific studies (e. g. B. Mechanical Engineering, Industrial Engineering, Automation Technology, Computational Engineering Science (CES), Applied Polymer Science)
- A basic physical understanding of plastic extrusion or 3D printing (FDM/FFF) is an advantage.
- Experience in programming with Python
- You enjoy the interface between classic mechanical engineering, software development and modern methods of digitalization (AI/ML).
These are your benefits:
- Work in a young, motivated team
- Independent work with intensive support
- Co-design of current research projects
- Individual coordination of tasks and time frame
- Experimental work on ultra-modern industrial plants (industrial robotics)
- Fast processing, direct contact for questions during processing
- Varied mix of experimental work on the machine and software development (Python)
- Immediate start possible
If you are interested in writing your thesis at IKV and in this task, please get in touch with me. We agree on the exact scope of content and schedule individually.