Abstract
A two-dimensional Cellular Automata (CA) – Finite Element (FE) (CA-FE) coupled model has been developed to predict the microstructures formed during the laser melting of a powdered AA-2024 feedstock using the Additive Manufacturing (AM) process Selective Laser Melting (SLM). The presented CA model is coupled with a thermal FE model, which computes the heat flow characteristics of the SLM process. The developed model considers the powder-to-liquid-to-solid transformation, tracks the interaction between several melt pools within a melted track, and several tracks within various layers. It was found that the calculated temperature profiles as well as the simulated microstructures bared close resemblance with SLM fabricated AA-2024 samples. The developed model was capable of predicting melt pool cooling and solidification rates, the type of microstructure obtained, the size of the melt pool (with 14% error) and the heat affected zone, average grain size number (with 12% error) and the growth competition present in microstructures of components manufactured via SLM.
Highlights
Selective Laser Melting (SLM) is an Additive Manufacturing (AM) technology that in recent years has experienced notable increases in industrial uptake for the manufacture of end-use engineering components
Each of these numerical approaches attempts to develop an improved understanding of the physical phenomena that occur during the laser processing of a powder bed
SLM samples were created to validate the results of the numerical simulation
Summary
Selective Laser Melting (SLM) is an Additive Manufacturing (AM) technology that in recent years has experienced notable increases in industrial uptake for the manufacture of end-use engineering components. The SLM technology can be used to process a wide range of metallic alloys (e.g. nickel, titanium, aluminium based etc.). The melting process is rapid, fusing multiple layers successively together creates complex thermal histories within the material. According to Verhaeghe et al [25] it is crucial to understand the physical phenomena involved within the fabrication process in order to accurately control it. O. Lopez-Botello et al / Materials and Design 113 (2017) 369–376
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