Thesis: AI-based local quality prediction for laser powder bed fusion

In cooperation with the Chair of Optical Systems Technology (TOS), the Fraunhofer Institute for Laser Technology ILT—Europe’s leading center for contract research and development in the field of laser technology—is offering the opportunity to write a thesis on the topic: »AI-based local quality prediction for laser powder bed fusion«. We are currently developing machine learning-based approaches to make the laser powder bed fusion (LPBF) process more efficient and improve its quality. To this end, a scalable database of sensor and process data is being created, which serves as the basis for ML models for component segmentation, strategic path planning and process optimisation. The aim is to enable data-driven solutions for the automated analysis of melt pools, porosity and other quality-relevant parameters in 3D printing.

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You will develop a modular data structure for storing sensor and metadata In doing so, you will create a simple surrogate model for mapping quality-related parameters For validation purposes, you will conduct initial test series and analyse the results Finally, you will check whether the model can reliably predict the parameters of a demonstrator component