  Laser powder bed fusion (LPBF) is a widely used metal additive manufacturing
technology. However, the accumulation of internal residual stress during
printing can cause significant distortion and potential failure. Although
various scan patterns have been studied to reduce possible accumulated stress,
such as zigzag scanning vectors with changing directions or a chessboard-based
scan pattern with divided small islands, most conventional scan patterns cannot
significantly reduce residual stress. The proposed adaptive toolpath generation
(ATG) algorithms, aiming to minimize the thermal gradients, may result in
extremely accumulated temperature fields in some cases. To address these
issues, we developed a deep reinforcement learning (DRL)-based toolpath
generation framework, with the goal of achieving uniformly distributed heat and
avoiding extremely thermal accumulation regions during the LPBF process. We
first developed an overall pipeline for the DRL-based toolpath generation
framework, which includes uniformly sampling, agent moving and environment
observation, action selection, moving constraints, rewards calculation, and the
training process. To accelerate the training process, we simplified the
data-intensive numerical model by considering the turning angles on the
toolpath. We designed the action spaces with three options, including the
minimum temperature value, the smoothest path, and the second smoothest path.
The reward function was designed to minimize energy density to ensure the
temperature field remains relatively stable. To verify the effectiveness of the
proposed DRL-based toolpath generation framework, we performed numerical
simulations of polygon shape printing domains. In addition, four groups of thin
plate samples with different scan patterns were compared using the LPBF
process.
