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Intricate dynamics in ultra-short pulse laser ablation of metals necessitate deeper process understanding. This work presents an auspicious contribution in this direction, incorporating a sophisticated three-dimensional finite-volume simulation tool, building upon our previously validated models in continuous-wave laser material processing. Atop the existing multiphysics framework, a two-temperature and a Drude absorption model has been integrated. Focused on laser ablation of copper, preliminary results unveil novel perspectives on the dynamic interaction between thermal effects and material response. Surpassing limitations inherent in one- and two-dimensional descriptions, this model distinctively captures the complex interplay of electron heating, energy transfer to the lattice, and ensuing rapid phase transitions. Crucially, it complements existing insights into ablation mechanism by illustrating the influence of elusive temperature-dependent material properties, such as density and the bulk modulus for superheated liquid metal, on the process. While still in development, these advancements mark a promising stride towards a deeper process comprehension.

This study extends an existing comprehensive computational framework to gain insight on hot cracking in the simulation of laser-based additive manufacturing via powder bed fusion. A novel approach to predict hot crack susceptibility based on vapor cavitation, building on a conceptual model akin to the Rappaz-Drezet-Gremaud criterion is introduced. Unlike conventional practices involving ex-situ evaluation of a criterion, the proposed model emerges implicitly from the underlying multiphysical modeling framework. The model exhibits sensitivity to variations in both material attributes (e.g., alloy composition) and processing conditions (e.g., laser beam shape or scanning strategy). Furthermore, non-equilibrium solidification is incorporated in the underlying Mass-of-Fluid framework, and a model for multi-layer printing is introduced to extend the length and time scale, enabling the derivation of detailed thermal histories on near-part-scale level. Consequently, the framework proves pivotal in optimizing process parameters, transcending the limitations inherent in conventional single melt track computational experiments.