Insights into phase transformations during selective laser melting of Ti6Al4V: a numerical approach

Gasser Abdelal, Daniel Higgins, Chi Wai Chan, Brian G Falzon

Research output: Contribution to journalArticlepeer-review

Abstract

Selective Laser Melting (SLM) is a transformative additive manufacturing technique that enables the production of complex metallic components with high precision. Understanding the microstructural evolution during the SLM process is crucial for optimising the mechanical properties and performance of the fabricated parts. This study focuses on developing a predictive model for the microstructure evolution of Ti6Al4V alloy during SLM. The model integrates thermal simulations with phase transformation kinetics using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) theory to predict the formation and dissolution of alpha and martensite phases. The thermal history of the SLM process was simulated using Finite Element Analysis (FEA) in Abaqus, which provided the temperature distribution and cooling rates experienced by the material. These thermal profiles were then used to drive the microstructure evolution model, which predicts the phase fractions and grain structures resulting from the SLM process. The model was validated against experimental data, showing good agreement in predicting phase fractions and microstructural features. Our results highlight the significant impact of processing parameters, such as laser power and scanning speed, on the microstructure of Ti6Al4V. Higher laser powers and slower scanning speeds were found to promote the formation of coarser microstructures, while faster cooling rates led to finer grains and higher martensite fractions. This comprehensive modelling approach provides valuable insights into optimising SLM process parameters to achieve desired microstructural characteristics and improve the mechanical performance of Ti6Al4V parts. The developed model is a robust tool for guiding the design and optimisation of SLM processes, reducing the reliance on trial-and-error methods, and enhancing the efficiency and quality of additive manufacturing for critical applications in aerospace, biomedical, and automotive industries.
Original languageEnglish
Number of pages17
JournalApplied and Computational Mechanics
DOIs
Publication statusE-pub ahead of print (In Press) - 2024

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