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

Fraunhofer-Gesellschaft; Deutschland; Vor Ort; EUR 40.000 - 60.000; Teilzeit

Zusammenfassung

Ein führendes Forschungsinstitut in Deutschland sucht einen Studierenden zur Entwicklung von maschinellen Lernansätzen zur Effizienzsteigerung in der additiven Fertigung. Du wirst ein Datenstrukturmodell erstellen und Testreihen durchführen. Du solltest Kenntnisse in Python und ein Ingenieurstudium haben. Es wird ein kreatives Teamumfeld mit flexiblen Arbeitszeiten angeboten.

Leistungen

Flexibles Arbeiten

Moderne Ausstattung

Interdisziplinäres Team

Gute Verkehrsanbindungen

Qualifikationen

• Du bist im technischen Studiengang (z.B. Maschinenbau, Informatik).
• Erste Kenntnisse in Python und Datenwissenschaft sind erforderlich.
• Eine strukturierte und eigenständige Arbeitsweise ist wichtig.

Aufgaben

• Entwicklung einer modularen Datenstruktur für Sensor- und Metadaten.
• Erstellung eines Surrogatmodells zur Abbildung qualitätsrelevanter Parameter.
• Durchführung von Testserien zur Validierung und Ergebnisauswertung.

Kenntnisse

Python-Programmierung

Datenwissenschaft

Signalverarbeitung

Organisationsfähigkeit

Ausbildung

Studiengang in Maschinenbau, Informatik oder einem vergleichbaren Ingenieurstudium

Jobbeschreibung

The Chair of Optical Systems Technology (TOS) at RWTH Aachen University is collaborating with the Fraunhofer Institute for Laser Technology ILT on pioneering projects in the field of additive manufacturing and laser material processing. 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.

What you will do

• 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

What you bring to the table

• You are studying industrial engineering, physics, computer science, mechanical engineering, computational engineering science or a comparable engineering degree programme
• You are already familiar with programming in Python and have gained initial insights into data science and signal processing
• Your working style is characterised by independence, structure and good organisation

What you can expect

Team spirit: Creative, interdisciplinary environment with a motivated team, flat hierarchies, and a casual culture.
Workplace: Excellent equipment with machines and devices as well as top-notch office facilities. Co-design: Practice-oriented thesis in an innovative environment of applied research. Work-life balance: Benefit from flexible working hours based on trust. + Benefits: Opportunity to take advantage of exciting corporate benefits. Accessibility: Good transport connections, parking spaces directly in front of the institute, an E-charging station, and a parking garage.

The position is temporary until completion of the final thesis.

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled people are given preference if they are equally qualified.

Interested? Then we look forward to receiving your application and getting to know you!
Did you already know? We give you even more insights on our LinkedIn profile.

Questions about this position will be answered by:
Julius Neuß M.Sc.
[phone]

Luke Schüller M.Sc.
[phone]

Fraunhofer Institute for Laser Technology ILT

www.ilt.fraunhofer.de

Requisition Number: 80738 Application Deadline: 09/30/2025