Experimental dataset for porosity, tensile properties, and fatigue performance of laser powder bed fused Ti-6Al-4V: intra-build and inter-build variability study

## Dataset Description This dataset contains specimen-level experimental data generated during a study investigating the intra-build stability and inter-build repeatability of porosity, tensile properties, and fatigue performance in laser powder bed fusion (LPBF) of Ti–6Al–4V. The dataset includes: - Build-level manufacturing information- Porosity measurements- Tensile test results- Fatigue test results The data were used to evaluate variability within individual build (called stability) and between nominally identical builds (called repeatability) of samples manufactured using nominally identical processing conditions. Associated Original Publication Reijonen, J., Vaajoki, A., Puukko, P. Solin, J., Alhainen, J., Kivistö, A. Intra-build stability and inter-build repeatability of porosity, tensile properties and fatigue performance in laser powder bed fusion of Ti–6Al–4V. Progress in Additive Manufacturing (2026). DOI: 10.1007/s40964-026-01772-y ## Experimental Overview Ti–6Al–4V specimens were manufactured using laser powder bed fusion under nominally identical process conditions in multiple independent builds. The study investigated: 1. Within-build variability in one build job called stability, with specimens grouped into five locations (1-5) on the build plate: center and all four corners2. Build-to-build repeatability in three build jobs called R1-3, with specimens grouped to two locations 3, 5 that were identified to be the best and worts location groups in step 1. The specimen population included tensile, fatigue and porosity specimens in each said location group. Detailed manufacturing procedures, specimen geometries, location of each individual specimen, testing methods, and statistical analyses are described in the associated publication. ## Data Reuse Users are encouraged to: - Reproduce the statistical analyses reported in the associated publication- Investigate spatial variability within LPBF builds- Develop statistical or machine-learning models for property prediction- Compare LPBF process stability across systems, materials, and laboratories- Perform alternative fatigue-life analyses accounting for censored observations ## License Creative Commons Attribution 4.0 International (CC BY 4.0). The original article and this reseach data set is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. For more details see readme.txt