Evaluation of the temperature history during extrusion based additive manufacturing

Additive manufacturing (AM) is considered as a key technology for the efficient production of individualized components. The technique enables the moldless production of complex geometries and designs that could not be realized cost-effectively with conventional manufacturing methods. The focus of the presentation is on the Fused Filament Fabrication (FFF), where a thermoplastic filament is extruded trough a nozzle. The material is deposited layer by layer until the final part is build. Thereby, high temperature gradients occur within the part when the hot material is deposit on the lower layers. Furthermore, the lower layers are reheated several times. The thermal history has major effects on crucial material properties such as degree of crystallization or viscosity. An insufficient viscosity can lead to a weak bonding and an inadequate degree of crystallization may result in a too low stiffness. Additionally, the temperature gradients cause residual stresses which are partly relaxed to part deformations. The remaining stresses can lead to premature failures. To achieve a high and repeatable part quality as well as a low scatter in the final part dimension an in-depth process understanding is required considering the causal material-process-part-interaction. In order to analyse these complex Multiphysics processes, manufacturing process simulations are suitable method. The objective of this work is to evaluate the prediction accuracy of currently available AM process simulation tools. Therefore, an AM process simulation of a cuboid is performed using the Abaqus AM plug-in. The cuboid made of PETG is printed on a Prusa i3 MK3. In order to monitor the temperature history during the printing process three very thin thermocouples (0.25mm) are integrated in the center and distributed over the part thickness. The machine code (gcode) is transferred to Abaqus and the transient temperature fields are predicted in dependency on the thermal boundary conditions. The predicted temperatures are compared to the measurements and the effect of thermal boundary conditions evaluated