Real-Time Thermal State Estimation and Forecasting in Laser Powder Bed Fusion

Abstract

Laser Powder Bed Fusion (L-PBF) is a widely adopted additive manufacturing process for fabricating complex metallic parts layer by layer. Effective thermal management is essential to ensure part quality and structural integrity, as thermal gradients and residual stresses can lead to defects such as warping and cracking. However, existing experimental or computational techniques lack the ability to forecast future temperature distributions in real time, an essential capability for proactive process control. This paper presents a real-time thermal state forecasting framework for L-PBF, based on a physics-informed reduced-order thermal model integrated with a Kalman filtering scheme. The proposed approach efficiently captures inter-layer heat transfer dynamics and enables accurate tracking and forecasting of spatial and temporal temperature evolution. Validation across multiple part geometries using measured data demonstrates that the method reliably estimates and forecasts peak temperatures and cooling trends. By enabling predictive thermal control, this framework offers a practical and computationally efficient solution for thermal management in L-PBF, paving the way toward closed-loop control in L-PBF.