4.8 Article

On study of process induced defects-based fatigue performance of additively manufactured Ti6Al4V alloy

期刊

ADDITIVE MANUFACTURING
卷 60, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.addma.2022.103227

关键词

Additive manufacturing; Titanium; Heat-treatment; Fatigue; Defects

资金

  1. Material and Manufacturing (M&M) Panel of Aeronautics Research and Development Board (ARDB)-DRDO [ARDB/01/2031951/M/I]

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Additively manufactured Ti6Al4V alloys have high strength but suffer from poor ductility and inherent defects. This study investigates the influence of defects on mechanical and fatigue properties of the alloy and optimizes them through heat treatment.
Additively manufactured Ti6Al4V are high strength alloys but associated with inferior ductility and inherent defects. These process-induced defects act as a stress raiser and deteriorates the mechanical and fatigue properties of the alloy. Post-processing is generally done to enhance the ductility and fatigue properties but at the expense of strength of the additively manufactured Ti6Al4V alloy. In this study, the effect of defects on mechanical properties and fatigue performance of additively manufactured (selective laser melting) Ti6Al4V alloy has been investigated based on experimentally calibrated defects-based model. The as-built specimens were subjected to different heat treatments in order to optimize their mechanical properties and the most optimum heat treatment was then selected for investigation of changes in fatigue properties. The beneficial effect of heat treatment on mechanical properties was mainly associated with the reduction in fraction of a'-martensitic microstructure and increase in a-lamellae thickness. Most of the fatigue failures occurred from surface and sub-surface defects, owing to the high stress concentration factor associated with them, and improved fatigue lives as a result of optimum heat treatment. A process-structure-property (PSP) linkage has been established to predict the mechanical properties. Experimentally calibrated defects' based fatigue model has been formulated to predict the fatigue performance using the experimentally observed data i.e. the process induced defects' size and their locations.

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