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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Computational Materials for Qualification and Certification
Presentation Title Achievements, Challenges, and Opportunities of a Zone-Based Probabilistic Damage Tolerance Framework for AM Components
Author(s) James Sobotka, Erin DeCarlo, Carl Fauver, Sakshi Braroo, Michael Enright
On-Site Speaker (Planned) James Sobotka
Abstract Scope Additive manufacturing (AM) processes introduce a variety of potential material anomalies that reduce fatigue life performance under cyclic loading through the processes of crack formation, fatigue crack growth, and fracture. A zone-based probabilistic damage tolerance framework has been proposed by Gorelik (Int. J. Fatigue, 2017) to ensure the structural integrity of higher-criticality AM parts. This probabilistic framework enables analysts to quantify uncertainty inherent in AM materials and potentially accelerate qualification and certification efforts. In this presentation, we present an overview of recent activities to apply and to adapt this framework towards increasingly complex AM components by highlighting recent achievements, ongoing challenges, and opportunities to expand this capability for industrial applications within the engineering software DARWIN®.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Modeling and Simulation,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating the Qualification of New Structural Materials for High Temperature Nuclear Reactors With Physics- and Data-Driven Models
Achievements, Challenges, and Opportunities of a Zone-Based Probabilistic Damage Tolerance Framework for AM Components
Bayesian Modeling for Concurrent Process and Part Design for Large Scale Additive Manufacturing
Challenges in Prediction Microstructure Variability in SS316
Computational Materials for Qualification and Certification Steering Group and Community Vision Roadmap
Computational Materials Tools for Qualification and Certification: Technology Maturation Path
Parametrically Upscaled Model-Based Predictive Platform for Fatigue with Location-Specific Microstructural Linkages
Robust and Efficient Design of Additively Manufactured Alloys by Integrating Uncertainty Quantification and Modeling Using Generative AI
The Critical Roles of Verification, Validation, and Uncertainty Quantification for Qualification and Certification of Metal AM Components for the Aviation Industry
Towards a Computational Digital Twin of Metals AM

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