TUHH-DC1: Additive Manufacturing and AI

Supervisors: Associate Prof. Andrey Molotnikov (RMIT) and Distinguished Prof. Milan Brandt (RMIT) and Prof. Dr. Ing. Claus Emmelmann (TUHH) and Dr. – Ing. Dirk Herzog (TUHH)

Project 1: Federated Learning Framework for AM

The aim of this project is to use AI methods to correlate input (machine data, materials, part geometries etc.) with output (mechanical properties, geometrical accuracy etc.) along the process chain of an AM process. Expert knowledge shall be integrated in a hybrid AI approach that combines multi-agent systems and federated learning to enable researchers from different institutions to train the AI. This position shall define specifically the framework for the federated learning approach. 

Project 2: AM process simulation for AI training data

Artificial intelligence is able to predict AM part performance from input parameters such as material and machine set-up, once sufficiently trained. However, AI relies on big data sets to produce valid results, which can often not be realised in experiments due to high costs and time efforts. The goal of this project is to set up simulation for selected AM process steps in order to simulate training data for the AI.

Project 3: Materials model for AM processes

Metals undergo a complex process chain in Additive Manufacturing, involving phase changes as well as different temperature cycles in the solid state. This results in a complex process – microstructure – property relationship, that needs to be understood along the whole process chain. The goal of this project is to set up a material model for a selected metal alloy that covers all phases and conditions experienced along the process chain, and that can be used in subsequent simulations to predict part properties.

 

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Reference

TUHH-DC1

Research Areas

Advanced manufacturing and mechatronics, Artificial intelligence and Machine learning

Research Host

Hamburg University of Technology (TUHH)

PhD awarding institution/s

Hamburg University of Technology (TUHH) and RMIT University

Location

Germany

Status

Open Position

RMIT University

Other Positions

Supervisors

Associate Prof. Andrey Molotnikov (RMIT), Professor Dr.-Ing. Thomas Niendorf (UoK), Philipp Krooss (UoK) and Dr. Markus Weinmann (UoK)

PhD awarding institution/s

University of Kassel (UoK) and RMIT University

Location

Germany

Status

Open Position

Supervisors

Prof. Jenny Zhang (RMIT) and Prof. Jose Antonio Vilar-Fernandez (UDC) and José Luis González Leal (Navantia) and Prof. Amparo Alonso-Betanzos (UDC) and Prof. Vicente Díaz-Casas (UDC) and Prof. Marcos Miguez González (UDC)

PhD awarding institution/s

University of A Coruña (UDC) and RMIT University

Location

Spain

Status

Open Position

Supervisors

Prof. Anne-Laure Mention (RMIT) and Associate Prof. Carsten Nico Hjortso (UPCH) and Dr. Marie Smed (UPCH)

PhD awarding institution/s

University of Copenhagen (UCPH) and RMIT University

Location

Denmark

Status

Open Position

RMIT and many of the REDI partners are HSR4R certified
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034328.

Results reflect the author’s view only. The European Commission is not responsible for any use that may be made of the information it contains