Auburn Student Wins ICAM Competition for Fatigue Modeling
A doctoral student from Auburn University's National Center for Additive Manufacturing Excellence (NCAME) has won the student competition at ICAM 2025 for developing a novel fatigue modeling approach for 3D-printed materials.
The International Conference on Additive Manufacturing (ICAM) 2025 student competition saw a significant win for a doctoral student representing Auburn University's National Center for Additive Manufacturing Excellence (NCAME). The student presented a breakthrough in fatigue modeling, a critical area for understanding the long-term performance and reliability of additively manufactured components.
Fatigue, the weakening of a material caused by cyclic loading, is a major concern for parts produced through 3D printing, as the inherent microstructures and potential defects can influence their resistance to failure over time. The developed model aims to provide a more accurate prediction of how these materials will perform under repeated stress, which is essential for their adoption in demanding applications.
This research directly addresses the need for robust design and validation tools in the additive manufacturing sector. By improving the understanding of fatigue behavior, engineers can more confidently design and implement 3D-printed parts in industries where safety and durability are paramount.
The success at ICAM 2025 highlights the growing sophistication of research in additive manufacturing, particularly in areas that move beyond basic production to address complex material science and engineering challenges.
This fatigue modeling breakthrough is significant as it tackles a key barrier to widespread adoption of AM in critical applications. Accurate fatigue life prediction is essential for qualifying 3D-printed parts for aerospace, automotive, and medical devices, enabling more reliable designs and reducing the need for extensive physical testing. This research aligns with the broader push for robust simulation and validation in AM.
Edited by the news editor with AI from the original report — please refer to the original source.