Laser Powder Bed Fusion (LPBF) enables the fabrication of geometrically complex metal components, yet the process remains limited by insufficient understanding and control of local heat accumulation. Spatially varying thermal conditions induce fluctuations in melt pool geometry, promote defect formation, and degrade microstructural and mechanical consistency. This thesis investigates intra-layer heat build-up as a critical but largely unexplored contributor to LPBF process variability.
Through high-speed infrared thermography, the work experimentally quantifies the magnitude and spatial distribution of preheating within individual layers under varied scan strategies, laser powers, and scan speeds. The measurements reveal substantial and highly localized temperature elevations that strongly correlate with scan vector length and temporal laser scan patterns. To unify these observations, the Hatch Return Time (HRT) is introduced as a physically grounded metric that links laser kinematics to local thermal history and predicts preheating behavior across different scanning strategies and process parameters.
Building on these insights, the thesis develops numerical models that quantify the influence of initial temperature on melt pool morphology. Coupling these models with Design of Experiments (DoE) approaches enables efficient identification of dominant parameters governing melt pool dimensions, absorptivity, and lack-of-fusion defect formation in Ti-6Al-4V and 316L stainless steel. This chapter particularly highlights the severe influence of the preheating temperature on the aforementioned metrics. Finally, a multiscale feedforward optimization framework is proposed to mitigate geometry-induced heat accumulation. By adaptively adjusting laser power based on predicted thermal fields near complex features, the method reduces melt pool variability and improves dimensional accuracy without requiring in-situ feedback.
Taken together, this thesis demonstrates that intra-layer heat accumulation, in the vicinity of geometric features and in their absence, is both more severe and more spatially heterogeneous than commonly accounted for, and that it constitutes a major source of systematic process variability in LPBF. The proposed HRT metric, predictive models, and compensation strategies provide a practical foundation for more stable, thermally informed LPBF processing and represent a step toward intelligent, predictive thermal control in metal additive manufacturing.
