Laser beam shaping in additive manufacturing: multiphysics simulations and experimental investigations
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Additive manufacturing of metals has attracted significant industrial interest due to its unique ability to fabricate complex and self-assembled geometries that are unattainable by conventional manufacturing processes. From an academic perspective, recent advances in laser beam shaping have shifted research focus from traditional goals such as density and surface roughness optimization toward deliberate control of microstructural texture. Achieving microstructural control enables the production of site-specific and layer-wise tailored mechanical properties within additively manufactured components. It is widely recognized that melt pool morphology plays a critical role in determining microstructural features, including grain size, orientation, and texture. Consequently, the capability to manipulate laser beam profiles, ranging from conventional Gaussian beams to axisymmetric configurations such as ring or ring-spot beams, as well as arbitrary beam shapes, has opened new pathways for controlling melt pool dynamics and solidification behavior. Multiphysics simulations have emerged as a powerful and efficient tool for investigating the influence of laser beam patterns on melt pool behavior. These simulations have demonstrated strong agreement with both ex-situ and in-situ experimental observations. Key process variables obtainable from such models include melt pool flow characteristics, melt pool dimensions, solidification temperature gradients, and solidification growth velocities. Moreover, to validate the trend analysis through simulations, online monitoring of the melt pool dynamics is carried out using the SWIR thermal system, while alternating beam shapes using the CIVAN system. References: [1] M. Bayat, R. Rothfelder and K. Schwarzkopf. et al., 93., 104420. Additive Manufacturing (2024). [2] M. Bayat, O. Zinovieva and A. Zinoviev et al., 124., 358-361. Procedia CIRP (2024). [3] M. Bayat, O. Zinovieva and F. Ferrari et al., 138., 101129. Progress in Materials Science (2023).
