Abstract Modeling and control of the spatiotemporal temperature distribution (thermal history) in laser powder bed fusion (LPBF) is critical, because, the thermal history governs defect formation mechanisms, such as porosity, deformation, dross accumulation, recoater interactions, amongst others. This paper presents a coupled physics-based computational modeling and feedforward process control framework for regulating the thermal history in LPBF of complex parts. Existing LPBF process optimization relies on empirical parameter tuning by manufacturing and testing of simple, standardized coupons. 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 complex geometries often result in build failures and defects. To address this limitation, a rapid graph theory-based computational model was coupled to a feedforward control algorithm. The resulting model predictive control (MPC) approach maintains a constant target temperature across layers by autonomously adjusting the laser power, velocity and inter-layer time in silico prior to manufacturing was validated for a complex, topology-optimized Inconel 718 component. Compared to empirically optimized parts, MPC-based processing eliminated deleterious porosity, thermal-induced deformation, dross formation, and recoater contact-related damage. Further, MPC reduced the weight of support structures by 45%, and consequently, decreased the as-built part weight by 20%. This work highlights the potential of physics-based predictive control of the fundamental thermal phenomena, as opposed to empirical process optimization, to mitigate defects in complex LPBF parts, and accelerate their practical deployment.
Spadaccia et al. (Wed,) studied this question.