End-to-end path planning for homogeneous temperature fields in additive manufacturing

This study explores a novel approach to path planning in deposition-based additive manufacturing, integrating the frequently overlooked process-induced temperature fields. Currently, existing approaches either ignore temperature effects entirely or only consider them in small-scale problems due to the high computational cost involved in predicting them and the combinatorial nature of path planning optimization. To address these challenges, the present work proposes an optimization pipeline that involves deriving a reduced order model from a finite volume method model with balanced truncation, using an analytical function to model the heat input and, calculating the steady-state response of the system to an arbitrary path using the Laplace transformation. Then, the optimization is transformed into a sequential decision-making problem and approximated with Monte Carlo tree search. The pipeline is validated through computational and experimental results, demonstrating its efficiency in managing large and complex geometries, as well as its resilience in overcoming the challenges posed by the simulation to reality gap.