  Fast and efficient simulations of metal additive manufacturing (AM) processes
are highly relevant to exploring the full potential of this promising
manufacturing technique. The microstructure composition plays an important role
in characterizing the part quality and deriving mechanical properties. When
complete parts are simulated, one often needs to resort to strong
simplifications such as layer-wise heating due to the large number of simulated
time steps compared to the small time step sizes. This article proposes a
scan-resolved approach to the coupled thermo-microstructural problem. Building
on a highly efficient thermal model, we discuss the implementation of a
phenomenological microstructure model for the evolution of the three main
constituents of Ti-6Al-4V: stable $\alpha_s$-phase, martensite $\alpha_m$-phase
and $\beta$-phase. The implementation is tailored to modern hardware features
using vectorization and fast approximations of transcendental functions. A
performance model and numerical examples verify the high degree of
optimization. We demonstrate the applicability and predictive power of the
approach and the influence of scan strategy and geometry. Depending on the
specific example, results can be obtained with moderate computational resources
in a few hours to days. The numerical examples include a prediction of the
microstructure on the full NIST AM Benchmark cantilever specimen.
