  In context of laser powder bed fusion (L-PBF), it is known that the
properties of the final fabricated product highly depend on the temperature
distribution and its gradient over the manufacturing plate. In this paper, we
propose a novel means to predict the temperature gradient distributions during
the printing process by making use of neural networks. This is realized by
employing heat maps produced by an optimized printing protocol simulation and
used for training a specifically tailored recurrent neural network in terms of
a long short-term memory architecture. The aim of this is to avoid extreme and
inhomogeneous temperature distribution that may occur across the plate in the
course of the printing process.
  In order to train the neural network, we adopt a well-engineered simulation
and unsupervised learning framework. To maintain a minimized average thermal
gradient across the plate, a cost function is introduced as the core criteria,
which is inspired and optimized by considering the well-known traveling
salesman problem (TSP). As time evolves the unsupervised printing process
governed by TSP produces a history of temperature heat maps that maintain
minimized average thermal gradient.
  All in one, we propose an intelligent printing tool that provides control
over the substantial printing process components for L-PBF, i.e.\ optimal
nozzle trajectory deployment as well as online temperature prediction for
controlling printing quality.
