Thermal Modeling and Feedforward Control for Defect Mitigation in Laser Powder Bed Fusion Process

Abstract Modeling and control of the spatiotemporal temperature distribution (thermal history) in laser powder bed fusion (LPBF) is critical because the thermal history governs defects, such as porosity, poor surface finish, cracking, and deformation. This article presents a coupled physics-based computational modeling and feedforward process control framework for regulating the thermal history in LPBF-processed parts. Existing LPBF process optimization relies on empirical parameter tuning by manufacturing and testing of simple, standardized coupon geometries. Empirical coupon-based optimization inherently disregards the geometry-dependent effect of thermal history on defect formation. Consequently, process parameters optimized based on coupon studies, when used for manufacturing real-world components, often result in build failures and defects. To address this limitation, a rapid graph theory-based computational model was coupled to a feedforward control (FFC) algorithm. The approach is implemented for manufacturing a topology-optimized Inconel 718 aerospace component (GE bracket). The model-guided FFC approach maintains a constant end-of-cycle (interpass or interlayer) temperature across layers by adjusting the laser power and velocity. The processing parameters are adjusted in silico—offline and prior to manufacturing—within the thermal model. Compared to its empirically optimized counterpart, the FFC-processed GE bracket exhibited three characteristics favorable to functional integrity and production: (i) meltpool instability-induced porosity was not observed; (ii) thermal-induced deformation, dross formation, and recoater contact damage were significantly mitigated; and (iii) FFC-induced improvements enabled the part to be manufactured with 45% less support mass, resulting in a 20% reduction in the as-built part weight (with the part design unchanged). This work thus underscores the potential of physics-based control, as opposed to empirical optimization, to mitigate defects in LPBF parts and accelerate their practical deployment.