  This article proposes a novel high-performance computing approach for the
prediction of the temperature field in powder bed fusion (PBF) additive
manufacturing processes. In contrast to many existing approaches to part-scale
simulations, the underlying computational model consistently resolves physical
scan tracks without additional heat source scaling, agglomeration strategies or
any other heuristic modeling assumptions. A growing, adaptively refined mesh
accurately captures all details of the laser beam motion. Critically, the fine
spatial resolution required for resolved scan tracks in combination with the
high scan velocities underlying these processes mandates the use of
comparatively small time steps to resolve the underlying physics. Explicit time
integration schemes are well-suited for this setting, while unconditionally
stable implicit time integration schemes are employed for the interlayer cool
down phase governed by significantly larger time scales. These two schemes are
combined and implemented in an efficient fast operator evaluation framework
providing significant performance gains and optimization opportunities. The
capabilities of the novel framework are demonstrated through realistic AM
examples on the centimeter scale including the first scan-resolved simulation
of the entire NIST AM Benchmark cantilever specimen, with a computation time of
less than one day. Apart from physical insights gained through these simulation
examples, also numerical aspects are thoroughly studied on basis of weak and
strong parallel scaling tests. As potential applications, the proposed thermal
PBF simulation framework can serve as a basis for microstructure and
thermo-mechanical predictions on the part-scale, but also to assess the
influence of scan pattern and part geometry on melt pool shape and temperature,
which are important indicators for well-known process instabilities.
