Empirical fatigue life model based on micro-CT defect severity indicator for laser powder-bed additively manufactured Ti-6Al-4V

In high-criticality aerospace applications, the acceptance of additively manufactured (AM) Ti-6Al-4V components depends on overcoming the unpredictable fatigue performance caused by stochastic (random) process-induced lack-of-fusion (LOF) defects. This study aims to validate the detectability and accuracy of X-ray Micro Computed Tomography (µCT) data for assessing LOF defects and to develop a statistical-based empirical fatigue life model from the µCT data. The maximum defect severity indicator (DSI), based on defect size, geometry, and location from the µCT data, was used to determine detrimental defects and estimate fatigue life. Results show that µCT can characterize most detrimental LOF defects for laser powder-bed fusion process, but detection is challenging due to morphological characteristics. High spatial resolution is critical for LOF defect detection using µCT. The empirical model showed a high linear correlation between fatigue life and DSI when separating surface and internal LOF defects, providing a quick estimate of fatigue life from a µCT scan. However, further study is needed to address limitations, such as the specific µCT equipment used and the number of samples tested, and to determine the generalizability of the model to other AM materials and processes.