The growing adoption of additive manufacturing (AM) across industries underscores the need for a deeper understanding of the mechanical performance of 3D-printed components. While metal 3D printing can produce parts with relatively high strength, further research is essential to evaluate their durability in real-world applications. This study investigates the high-cycle fatigue behavior of 3D-printed 17-4 stainless steel specimens, comparing them to conventionally plasma-cut solid counterparts. The plasma-cut specimens are sourced from 17-4 stainless steel sheets, while the printed specimens are fabricated using Fused Deposition Modeling (FDM), an extrusion-based metal AM process. Specimens are printed in five orientations: 0°, 22.5°, 45°, 67.5°, and 90°, with standardized subsize test specimens designed according to ASTM E8/E8M-22. The study focuses on high-cycle fatigue behavior, providing fatigue properties for 3D-printed metal components to support real-world design applications.

Prior studies have been conducted using the more common metal AM method of Powder Bed Fusion, such as SLM, SLS, and DMLS, and while some fatigue life predictions models have been previously made, they lack the testing across multiple print orientations and do not go further than creating a P-S-N Curve. A P-S-N Curve is a set of probabilistic distribution curves to describe the fatigue strength of the material, though it requires massive amounts of data. This study aims to primarily focus on a high-cycle fatigue study, gathering data for a predictable S-N curve to further use for the Probabilistic Fatigue Damage Theory (K-D Model). The K-D Model uses the slope of an S-N curve to derive the Fatigue Strength Index (K0) which is used to develop a log-normal plot depicting the fatigue life of the material itself rather than its behavior at specific stress levels. Overall, the findings from this research will provide quantitative insight into the mechanical fatigue properties of 17-4 Stainless Steel 3D Printed components for fatigue critical applications such as aerospace, automotive and biomedical fields. By developing a clearer high-cycle fatigue life estimation of the 3D Printed parts compared to conventional plasma cut specimens, this research can further bridge the gap between additive and conventional manufacturing approaches.