Multi-scale modeling of metallurgical phenomena in metal laser powder bed fusion additive manufacturing: A comprehensive review

Abstract

Laser Powder Bed Fusion (LPBF) has transformed additive manufacturing, enabling the production of intricate, high-performance components across aerospace, automotive, and biomedical industries. This review provides a novel analysis of the multi-scale metallurgical phenomena governing LPBF, addressing critical gaps in heat transfer dynamics, microstructural evolution, and residual stress formation. It highlights underexplored factors, including the interplay of laser-material interactions, thermal conductivity, and specific heat capacity, and their combined effects on rapid cooling rates and phase transformations. Advanced microstructure implementation strategies are explored, emphasizing the relationships between laser scanning speed, melt pool geometry, cooling rates, and grain morphology. Predictive models, such as phase field simulations, austenitization, and martensite transformations, are reviewed, with a focus on nucleation mechanisms and grain refinement to mitigate defects and optimize performance. The review evaluates advanced modeling approaches that integrate thermal, mechanical, and metallurgical aspects, such as phase-field and finite element models, for defect prediction and process optimization. The transformative potential of in-situ monitoring techniques, including thermal imaging and melt pool analysis, is emphasized for their ability to correlate process parameters with metallurgical outcomes. Emerging trends like machine learning and multi-physics simulations are identified as pivotal for addressing challenges in parameter tuning and adaptive process control. By proposing a roadmap for comprehensive multi-scale modeling, real-time monitoring integration, and material development tailored for LPBF, this review advances the understanding and scalability of LPBF technology, ensuring its impactful application in high-demand manufacturing sectors.