Additive Manufacturing technologies (AMT) and their achievements have drawn a lot of attention over the previous 10 years. However, to implement the high potential of such technologies on a widespread scale, there are still a few demands that need consideration. This includes for example the creation of suitable design tools that will help designers in capitalizing on the possibilities presented by Additive Manufacturing (e.g., broad design flexibility, consumer and patient-specific designs, digital materials, etc.). Additionally, the materials utilized and workpiece properties achieved must meet the demanding requirements of applications e.g., in medicine or industry (thermomechanical characteristics, repeatability, and cost, etc.).

“DigiPhot” seeks to address the scientific challenges associated with the aforementioned subjects by offering a set of PhD projects that follows the experience of the participating partners in FH Campus Wien and TU Wien. The project is divided into four distinct topics, each of which is represented in form of a PhD thesis. These topics include advanced methods for characterizing nanostructured additive manufacturing materials, novel tools for the generative design of additive manufacturing parts, methods for online monitoring of laser-based additive manufacturing processes, and process simulation of selective laser melting. Each of the four sub-projects meant to reflect the supervisors' and sub-project leaders' expertise and research interests, with the aim of maintaining the focus of the particular research groups and enhancing their international visibility. Merge across the projects ensures a consistent frame around the overall project.

Project 1: Fracture mechanical analysis of heterogeneous photopolymers for additive manufacturing

Project 2: Process simulation for laser-assisted additive manufacturing

Project 3: Generative design for Selective Laser Sintering and Hot Lithography

Project 4: Development of In-Situ measurement methods for monitoring and recording printing errors to predict part quality