Prediction of microstructure in laser powder bed fusion process
Source abstract
Abstract
Abstract Additive manufacturing (AM) processes are receiving widespread attention due to the ability to create or repair precision engineering components without use of any die or mold. Currently, the approach to obtain a specific user defined/as-desired or conformal/epitaxial microstructure is a challenging and expensive iterative process. Modeling and validation of solidification microstructure can be leveraged to reduce iteration cost in obtaining a desired microstructure. Numerical Volume-of-fluid based method incorporating Marangoni convection can accurately predict the resultant melt pool geometry and temperature distribution which can serve as an input in prediction of microstructure evolution in solidifying mushy region. Hence, in the present study, computational fluid dynamics (CFD) analysis is used to predict melt pool characteristics and phase field modeling is employed to simulate microstructure evolution in the as-deposited state for laser powder bed fusion (LPBF) process. Different features of LPBF microstructure such as segregation of secondary elements, dendrite sizes, dendritic orientation, dendritic morphology, and surface roughness are investigated and validated through comparison with experimental results. Phase-field model suggests strong dependency of dendrite orientation on surface roughness and scan speed and suggests potential of columnar flip or oriented-to-misoriented transition at higher scan speed. Segregation of the secondary elements is found to be the dominant factor in resultant dendrite width in the range of 1–3 μm. Furthermore, the developed method can easily be extended to predict the change in orientation of dendrites as new layers are built atop previous layers.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Solidification and crystal growth phenomena · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
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