Authors:
Lennard Hermans (Fraunhofer IAPT, Germany)
Jan Scheumann (Fraunhofer IAPT, Germany)
Burhan Umur Avci (Fraunhofer IAPT, Germany)
Ingomar Kelbassa (Institute for Industrialization of Smart Materials (ISM), Hamburg University of Technology TUHH, Germany)
Abstract:
A time-intensive challenge in material extrusion is determining process parameters for metal injection molding feedstock that enable the production of high-density components. A critical parameter is the extrusion temperature, which significantly influences the feedstock viscosity and, consequently, the material flow rate. Through in-situ measurement of the flow rate during continuous extrusion, both the initial extrusion temperature and the changes in feedstock viscosity can be determined by successively increasing the temperature. The feedstock-specific material flow rate during extrusion must be precisely adjusted to achieve the required line width in continuous deposition. Even minimal deviations in line width or feedstock viscosity can lead to bonding and layer structure defects. In this paper a closed-loop algorithm that efficiently automates time-consuming and experience-based calibration in piston-based extrusion is presented. Following the process parameter determination, ranges of suitable extrusion temperatures along with the corresponding feedstock viscosity values are provided via datasets, enabling faster production cycles.
DOI:
https://doi.org/10.59499/EP256765584
