Additive manufacturing
Additive manufacturing (AM) or 3D-printing has the potential to transform the regime of manufacturing as we know it today. To fully utilize the potential of the technique a more fundamental knowledge of the process is required.
To fully utilize the potential of the technique with AM a more fundamental knowledge of the process is required. Within this project, we aim to advance the knowledge by developing generic experimental and computational methods to increase the understanding of process parameters, microstructure and properties in AM of high-performance metallic materials. We will apply the methods on two demonstrator systems; Ni-based super alloys (Alloy 625 & 718) and amorphous alloys.
Within the project, we will:
• Develop generic methods for modelling of AM processes
• Develop methods for rapid screening of microstructure and properties of AM gradient samples to reduce lead time in research and industrial production
• Evaluate microstructure and properties of demonstrator systems including a comparison between different AM methods
• Demonstrate the generic use of the models and screening methods by the AM of two components, one based on Ni-based materials and another on amorphous alloys
Challenging task
The work at LTU includes development of a mechanism based material model for alloy 625. The development includes material testing and calibration of model parameters. The work also includes development of models for simulation of AM. This is a challenging task as the manufacturing method includes many layers and even more weld lines on each layer. The approach includes lumping of layers and consolidation of the heat source to string-by-string or layer-by-layers. The work at LTU also includes material modelling of the thermal behaviour of a metallic glass. The aim is to predict the resulting phase structure as it develops during AM in order to tailor the process parameters for crystalline free structure. To do this, the temperature history is first computed in the proximity of the laser spot and then used as input to a phase transformation model.
