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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Additive Manufacturing Modeling, Simulation and Artificial Intelligence
Presentation Title A Machine Learning-Based Model for Fatigue Behavior Prediction and Analysis of Surface Treated Laser Powder Bed Fused Nickel Based Super Alloys
Author(s) Erfan Maleki, Shuai Shao, Nima Shamsaei
On-Site Speaker (Planned) Erfan Maleki
Abstract Scope The surface anomalies associated to as-built state of additively manufactured (AM) parts can be significantly modified/eliminated by applying surface post-treatments (SPTs). In this study, firstly effects of various SPTs on fatigue behaviors of laser powder bed fused (L-PBF) nickel based super alloys of IN625, Haynes 282, and Haynes 214 were examined through experimental investigation. Different SPTs including mechanical and chemical treatments were considered. Comprehensive experiments were carried out to analyze the surface texture, microstructure, residual stresses, and uniaxial fatigue behavior. Afterwards, a machine learning-based model was developed using the experimental results for fatigue behavior prediction as well as sensitivity and parametric analyses. Influencing structural parameters were considered as the inputs of the model and fatigue life was regarded as the output. The results revealed that, according to the life regime, the effects of influencing factors can vary considerably. Additionally, surface residual stress exhibited the highest effects on fatigue life variation.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Machine Learning, Surface Modification and Coatings

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