Porosity determination in additively manufactured Ti parts using X-ray tomography
J. Glinz, M. Reiter, T. Gastinger, A. Huskic, B. Plank, J. Kastner, S. Senck - Porosity determination in additively manufactured Ti parts using X-ray tomography - 9th International Conference on Industrial Computed Tomography (iCT) 2019, Padova, Italy, 2019, pp. 1-7
Ti6Al4V is a suitable titanium alloy for all kinds of medical implants and prostheses because of its high durability and biocompatibility. Furthermore, components of high complexity can be produced via additive manufacturing which allows for more flexibility and easy prototyping of patient specific implants. However, this flexibility implies the risk of internal defects resulting from the manufacturing process. The nondestructive investigation of critical components is therefore crucial to avoid premature failure. X-ray micro-computed tomography (XCT) is a method that can resolve internal structures three dimensionally in a non-destructive way. Nevertheless, the probability to detect defects is limited by the achievable resolution and image quality of a scan. In this contribution, we performed a systematic study to determine the pore size distribution in additively manufactured Ti6Al4V parts using XCT. We focused on the influence of scanning parameters such as voxel size, tube voltage and current on the image quality that determines the outcome of the porosity analysis. Image quality was assessed via contrast to noise ratio (CNR) and slanted-edge modulation transfer function (MTF) according to ISO 12233. Furthermore, we optimized the beam hardening correction for the scans and investigated influences of different image denoising algorithms. Results showed that tube voltage and current greatly influence the CNR of the data set while the MTF is, within limits, almost constant as long as the electron beam focus is optimized. With higher physical resolution, smaller defects can be detected, which leads to porosity values of 0.36, 1.35 and 2.54% at 10, 5 and 2.5 µm resolution respectively. Image post-processing can further influence porosity outcome because of the segmentation of noise induced particles. Different image denoising algorithms therefore can heavily reduce porosity values depending on spatial resolution.