| Abstract Scope |
In Laser Powder Bed Fusion (LPBF), part quality significantly depends on thermal conditions related to local geometry, impacting defect formation and microstructure. Due to the substantial scale difference between the laser beam (µm) and the part (cm), conducting centimeter-scale scanwise simulations becomes nearly impossible, often requiring weeks or even months because of the intense computational demands. This study introduces a surrogate model for efficient time-dependent, scanwise thermal simulations in LPBF. The model comprises image processing, deep neural networks, and LSTM (Long Short-Term Memory) units. Its input vector includes a range of features, indicating both scanning strategy and local conductance of various geometrical features, alongside time-dependent heat source locations and preheat temperatures. Achieving more than a 200x computational speedup, this model significantly enhances the feasibility of simulating thermal history, defect, and microstructure formation in centimeter-scale LPBF parts. |