A Review of Meltpool Dynamics and Grain Evolution in Inconel Alloys Produced by Laser Powder Bed Fusion

Abstract

Laser powder bed fusion (LPBF) is a disruptive additive manufacturing process for producing high-performance Inconel superalloy parts with complex shapes for the aerospace, energy, and other demanding industries. However, uniform part quality remains a persistent challenge, as process parameters, melt-pool dynamics, microstructural evolution, defect formation, and mechanical performance are closely coupled across a wide range of spatial and temporal scales. In previous reviews, these dimensions have been considered in isolation with limited insight into their interactions and implications for predictive process control. The present review aims to address this lacuna by proposing a unified Process–Structure–Property–Control (PSPC) framework for LPBF-produced Inconel 625, 718, and 738. The discussion begins with material attributes governing alloy processability, and then synthesises the melt-pool physics governing thermal behaviour, solidification, and energy transfer. Attention then turns to a critical assessment of grain evolution, defect formation, and process stability, showing how the thermal history governs microstructural development and, in turn, mechanical performance via linked process–structure–property relationships. Progress in multiscale numerical modelling, such as finite-element analysis, computational fluid dynamics, phase-field modelling, cellular automata, and phase-diagram calculation (CALPHAD), is reviewed to establish a comprehensive modelling ecosystem for predictive LPBF. The review also discusses the potential of emerging technologies, such as beam shaping, multi-laser processing, in situ monitoring, artificial intelligence, and powder recyclability, to increase the robustness and productivity of the process. Building on these advances, a digital-twin-enabled predictive-manufacturing framework that integrates physics-based models, data-driven algorithms, and real-time monitoring is introduced to enable closed-loop process optimisation. The review ends with a scientific synthesis and future research roadmap for intelligent, reliable, and autonomous LPBF of next-generation Inconel superalloys.