Knowing and managing the fatigue performance of Ti6Al4V alloy when produced using Electron Beam – Powder Bed Fusion (EB-PBF) is essential due to its extensive use in demanding aerospace and medical applications. The main goal of this study is to systematically evaluate how beam current and scan speed affect the tensile strength and fatigue behaviour of Ti-6Al-4V produced by EB-PBF. To isolate their impacts, the machine was operated in manual mode with constant parameter settings, in contrast to standard EB-PBF operations. The findings unequivocally showed that the different process parameters and the resulting microstructure and fatigue properties were directly correlated. Denser materials with lower porosity were produced by higher energy densities, which were attained by particular beam current and scan speed combinations. This greatly improved the specimens’ mechanical characteristics and fatigue performance. These conclusions lay the basis for future optimisation, potentially leveraging artificial intelligence to estimate and attain desired material characteristics under stress while minimising manufacturing defects.

Fatigue crack growth in EB-PBF processed titanium alloys: impact of manufacturing parameters

Bellini, C.
;
Di Cocco, V.;Franchitti, S.;
2026-01-01

Abstract

Knowing and managing the fatigue performance of Ti6Al4V alloy when produced using Electron Beam – Powder Bed Fusion (EB-PBF) is essential due to its extensive use in demanding aerospace and medical applications. The main goal of this study is to systematically evaluate how beam current and scan speed affect the tensile strength and fatigue behaviour of Ti-6Al-4V produced by EB-PBF. To isolate their impacts, the machine was operated in manual mode with constant parameter settings, in contrast to standard EB-PBF operations. The findings unequivocally showed that the different process parameters and the resulting microstructure and fatigue properties were directly correlated. Denser materials with lower porosity were produced by higher energy densities, which were attained by particular beam current and scan speed combinations. This greatly improved the specimens’ mechanical characteristics and fatigue performance. These conclusions lay the basis for future optimisation, potentially leveraging artificial intelligence to estimate and attain desired material characteristics under stress while minimising manufacturing defects.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/125443
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