Conference Logo ProgramMaster Logo
Conference Tools for 2026 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract

Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Computational Materials for Qualification and Certification
Presentation Title Computational Materials for Qualification and Certification Steering Group and Community Vision Roadmap
Author(s) Edward Glaessgen, Michael Gorelik, Lyle E. Levine
On-Site Speaker (Planned) Lyle E. Levine
Abstract Scope The Computational Materials for Qualification and Certification (CM4QC) Steering Group is a recently assembled team from U.S. industry, government, and academia that is exploring ways of maturing Computational Materials (CM) framework capabilities to enable their use in the context of qualification and certification (Q&C) of metallic process intensive materials (PIM) for aeronautics applications, including metal additive manufacturing (AM). CM4QC will inform the industry and the certifying agencies on how to enhance current Q&C practices through the insertion of CM capabilities in aeronautical metallic PIM components/applications. Additionally, CM4QC will identify technical and regulatory considerations that should enable a broader use and acceptance of CM methods within a Q&C framework. In this presentation, we will provide an overview of the CM4QC steering group and its community vision roadmap including identification of regulatory gaps, enablers, and requirements; identification of key CM and enabling technologies, assessment of current maturity levels and required future development.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Additive Manufacturing, Other

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

Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement