Titanium–aluminium (Ti–Al) alloys, particularly α-β alloys such as Ti 6 Al 4 V, are widely used in industry thanks to their excellent mechanical properties and compatibility with additive manufacturing (AM) technologies such as Laser Powder Bed Fusion (PBF-LB). A key challenge in improving the reliability of processing these alloys is to understand and predict their microstructural evolution, which is highly sensitive to the thermal history, especially the cooling rate. The formation and distribution of β-, α- and martensitic α ′ -phases determine the phase composition of the material, thereby influencing key properties such as strength, residual stress and distortion of the final component. This work, [1], presents a thermodynamically consistent, fully thermomechanically coupled phase transformation framework that can predict microstructure evolution and resulting strains during PBF-LB processes. The model is based on free energy formulations and features a novel dissipation function that governs phase evolution kinetics. The function’s coefficients are determined using a limited number of continuous cooling transformation (CCT) diagrams in a parameter identification process. These diagrams are then reconstructed and validated using available experimental data. By capturing melt-solid-solid transitions, including the molten, β- and α- phases of Ti6Al4V, the framework provides a more realistic description of temperature-dependent phase fractions and strain contributions. The model has been applied to idealised cooling paths, as well as to thermal histories extracted from finite element (FE) simulations of PBF-LB simulations using Abaqus. The results demonstrate the framework’s ability to accurately predict microstructure and strain evolution under different cooling conditions with the aim of improving the predictive capabilities of AM processes. REFERENCES [1] Noll, I. and Bartel, T. and Menzel, A. A thermodynamically consistent phase transfor mation model for multiphase alloys: application to Ti 6 Al 4 V in laser powder bed fusion processes, Comput. Mech. (2024) 74:1319–1338. doi:10.1007/00466-024-02479-z