Conceitos essenciais
A deep reinforcement learning-based framework is developed to generate optimized toolpaths that achieve uniformly distributed temperature fields and avoid extreme thermal accumulation during the laser powder bed fusion process.
Resumo
The paper presents a deep reinforcement learning (DRL)-based toolpath generation framework for the laser powder bed fusion (LPBF) process. The goal is to achieve uniformly distributed temperature fields and avoid extreme thermal accumulation regions during printing.
Key highlights:
- A simplified numerical model is developed by considering the relationship between turning angles and thermal distributions to improve computational efficiency.
- The reward function is designed to minimize the input energy density, aiming to ensure a stable temperature field.
- Special environments, such as unqualified-points, isolated-points, and sensitive regions, are defined and handled during the training process.
- Numerical simulations for a polygon case study demonstrate the effectiveness of the DRL-based approach in obtaining uniformly distributed temperature fields and avoiding excessive thermal accumulation.
- Experimental results show that the DRL-optimized toolpath can reduce the maximum distortion by approximately 47% compared to zigzag patterns, 29% compared to chessboard patterns, and 17% compared to adaptive toolpath generation (ATG) patterns.
- The study presents a promising approach for using machine learning to optimize toolpath patterns in the LPBF process and provides a foundation for further research in this area.
Estatísticas
The maximum depth of the molten pool increases significantly as the turning angle decreases below 90 degrees, but remains constant around 45 μm when the turning angle is greater than 90 degrees.
Citações
"The elimination of extremely accumulated thermal regions will be accomplished by maximizing the reward function, which can be designed to minimize the energy density as much as possible."
"Our DRL-based approach provides a promising solution to address the problem of residual stress accumulation during the LPBF process."