Project runtime: 01.01.2023 – 31.12.2025
2023-09-22_Vollversammlung_Poster_DiMad
Additive manufacturing processes for metals involve considerable, predominantly experimental, effort when it comes to determining process windows for material systems or transferring suitable process parameters from one type of system to another. Accelerated development is only possible if the process chain from the starting materials to the component is defined in its entirety and set up digitally. New synergies between experimentation and digital methods such as process simulation can then be exploited to great advantage. The DiMad project is making a contribution by examining wire-based processes with the two heat sources of arc and laser radiation for selected high-alloy steels. From the user's point of view, the main characteristics influencing the quality and properties of the component are investigated in depth. Material properties, such as microstructure (composition of the metal on a microscopic level), are described by simulation. In addition, machine learning (ML) will be used for process window search and correlation between process, defect, and property, as well as for interpreting the totality of data from simulation, experiment, and analysis. Based on the digital tools, process parameters for the production of two real components will be predicted as demonstrators and then set up by the industrial partners. In intensive dialog with the MaterialDigital platform, a user ontology will be developed for the buildup welding processes of high-alloy wire materials under consideration.
