Fatigue of Additively Manufactured Nickel-based Superalloys in Harsh Environments

Fatigue accounts for approximately 90% of mechanical failures, particularly in components exposed to cyclic loading under harsh operating conditions. Nickel-based superalloys, renowned for their strength and oxidation resistance, are widely used in such demanding environments. Traditionally processed through casting, forging, and powder metallurgy, these alloys are now increasingly fabricated using additive manufacturing (AM), which offers advantages such as complex geometry production, reduced waste, and enhanced design flexibility. Among AM techniques, Wire Arc Additive Manufacturing (WAAM) stands out for its high deposition rates and ability to produce large components. However, its application in fabricating nickel-based superalloys for fatigue critical applications remains underexplored compared to other AM methods.While nickel-based superalloys such as Inconel 625, Inconel 718, and Hastelloy X have been extensively used in harsh operating environments, there is growing interest in next-generation alloys that could offer superior mechanical properties compared to those currently available. Haynes® 233, a recently developed alloy, is in its early commercialization phase, with limited data available on its mechanical and fatigue properties. This research investigates fatigue behavior of WAAM-processed Haynes® 233, focusing on the relationship between processing, microstructure, and mechanical and fatigue performance. The effects of processing techniques and high-temperature oxidation on fatigue properties are examined, and the underlying strengthening and deformation mechanisms are explored. As the first study on WAAM-processed Haynes® 233, these findings establish a baseline for understanding fatigue behavior of this alloy, providing a foundation for future research and optimization in high-performance applications within industries such as aerospace and energy. As AM gains traction in fatigue-critical applications, there is a growing need for robust predictive tools. However, the complex interplay of process variables makes modeling fatigue properties challenging. In this study, a data-informed knowledge discovery framework is developed to explore AM fatigue databases and predict fatigue properties from fatigue studies in the literature.

Author

• Alfred, Samuel 0nimpa

• Additive Manufacturing
• Fatigue
• Haynes 233
• Machine learning
• Mechanical testing
• Nickel-based superalloys

Identifier

hdl:1920/14816

OAI identifier oai:identifier

oai:MARS:1920/14816