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Meeting MS&T24: Materials Science & Technology
Symposium Computational Materials for Qualification and Certification
Sponsorship TMS: Computational Materials Science and Engineering Committee
TMS: Mechanical Behavior of Materials Committee
Organizer(s) Corbett C. Battaile, Sandia National Laboratories
Anthony D. Rollett, Carnegie Mellon University
Edward Glaessgen, NASA Langley Research Center
Michael Gorelik, Federal Aviation Administration
Scope This symposium will bring together practitioners and decision-makers from multiple sectors, i.e. industry, academia, and government, to discuss the state-of-the-art and paths-forward in development and adoption of computational materials technologies in industries’ qualification and certification (Q&C) activities. The primary objectives of the symposium are to provide a detailed overview of the key elements relevant to the development and adoption of computational materials technologies in Q&C activities; facilitate continued collaborative discussions among the industrial, regulatory, and scientific communities including identification of areas for government and industry R&D investments; discuss the needs and requirements of the federal agencies and regulatory bodies in this arena; and increase airworthiness and the certifying authorities’ awareness and acceptance of computational materials informed methods for Q&C of structural or flight-critical parts made with metallic process intensive materials (PIM).

The motivation for organizing this symposium arose from several events that occurred over the past four years. In mid-November of 2019, a Workshop on Materials and Methods for Rapid Manufacturing for Commercial and Urban Aviation, organized by NASA’s Aeronautics Research Mission Directorate, was held in Washington DC. Subsequently, a Technical Interchange Meeting on Computational Materials Approaches for Qualification by Analysis, organized by NASA, NIST, and the FAA, was held on January 15-16 2020 at NASA Langley Research Center. Largely as a result of these two meetings, in September 2020 the Computational Materials for Qualification and Certification (CM4QC) Steering Group (SG) was established, including 25 member organizations, with a mission to explore strategies for maturing and developing trust in Computational Materials (CM) capabilities for use in the qualification and certification (Q&C) of metallic PIM for aeronautics applications, including but not limited to metals additive manufacturing (MAM). The culmination of the group’s activities will be a roadmap document that is on track to be completed by the end of 2023. The roadmap is a community vision spanning topics related to the use of CM capabilities as a component of industry’s Q&C framework; identification of the relevant regulatory gaps, enablers and requirements, including acceptable levels of V&V; identification of key CM and enabling technologies, assessment of their current maturity levels, and required future development and opportunities for investment. Part of this symposium will entail presentations focused on the roadmapping activities of the SG.

We are seeking contributed abstracts focused specifically on the use of computational materials tools in qualification and certification activities, covering topical areas that include but are not limited to:

• Challenges and opportunities for the adoption and use of computational materials-informed approaches in the qualification and certification domain (including understanding of regulatory requirements / considerations and industry vision).
• Computational materials methods and capabilities that may support the industry vision for computational materials in the Q&C domain, with particular focus on their levels of maturity in that context.
• Verification, validation, and uncertainty quantification methods and capabilities that are needed to meet regulatory requirements.
• The computational materials ecosystem supporting the industry vision. Some elements of the ecosystem include: training, education, testing, data sources, standards (including best practice guides for how to use CM for Q&C), organizational culture change relative to broader adoption of CM, and examples of government / industry partnerships

In addition, we will devote a session to presentations on, and a discussion of, the activities of the CM4QC SG; and we will conduct a panel session to discuss the various topics covered in the symposium.

Abstracts Due 05/15/2024

A Computational Multiscale Approach for Predicting Macroscale Elastic Properties and Failure Initiation in Phenolic Impregnated Carbon Ablator
A Framework for Assessing Simulation Maturity
Additive Manufacturing Porosity Estimation Using Multiple Nondestructive Evaluation Techniques
America Makes Efforts in Advanced Qualification Methods for AM
Assessing the Impact of Melt Pool Geometry Variability on Lack-of-Fusion Porosity and Fatigue Life in Powder Bed Fusion - Laser Beam Ti-6Al-4V
Computational Framework for Spatially-Dependent Melt Pool and Microstructure Simulations of Additively Manufactured Material
Computational Investigation on the Combined Effect of Pore Attributes on Strain Concentrators in Metal Additively Manufactured Materials
Computational Materials for Qualification and Certification Steering Group and Community Vision Roadmap
Computational Tools for Advancing Materials Maturity in Additive Manufacturing
Convolution-Based Numerical Solutions of Transient Temperature Fields during Powder Bed Fusion Additive Manufacturing: Theory, Accuracy, and Computational Cost
Correlations of Additive Manufacturing Model-Based Process Metrics With Spatter-Induced Porosity in the Powder Bed Fusion-Laser Beam/Metallic Process
Data-Driven Process Uncertainty Analysis of Stochastic Lack-of-Fusion in Laser Powder Bed Fusion
Development of Computational Materials Workflows for Additively Manufactured Metallic Materials to Enable Accelerated Prediction of Fatigue Performance
Efficient Sensitivity and Uncertainty Analysis of a Laser Powder Bed Fusion Thermal Model Built Using HYPAD-FEM
Enabling Rapid Aerospace Component Qualification and Certification: Integrated Model-Based Material Definitions in Additive Manufacturing
Fast, Cheap & In Control: Application of Surrogate Models to Explore Microstructure-Properties Relationships for AM-Based Materials
Fatigue Performance and DADT Certification of Powder Bed Additively Manufactured Ti-6Al-4V: Defect Assessments, EIDS Distributions, and Inspection Limits
GO-MELT: GPU-Optimized Multilevel Execution of LPBF Thermal Simulations
Industry's Vision for the Use of Computational Materials Tools in Qualification and Certification
Lessons Learned Calibration and Validation of Process Models for Laser Powder Bed Fusion Additive Manufacturing
Machine Learning Enabled Parametrically Upscaled Constitutive Models for Fatigue Simulations: A Data-Driven Multiscale Modeling Approach
Materials Data for Validation and Verification of Mechanical Performance: Outcomes and Future Perspectives from the AM Benchmark Series
Physics-Based Modeling of Ti-6Al-4V Phase Transformations for PBF-LB Temperature Histories
Process sensitivity of Laser Powder Bed Fusion of IN718 to Composition Variation
Quantification of Microstructure-Induced Uncertainty in Fatigue Nucleation in Polycrystalline Materials
Quantifying Microstructure Evolution of LPBF Ni-Alloy Under High Temperatures Exposure Through Computer Vision
QUASAR – Assessment of the State of the Art and Gaps for AM of Fracture Critical Components
Review of Past and Future Impacts of the Additive Manufacturing Benchmark Test Series (AM Bench)
Scientific AI for Automated Validation and Certification
Towards a Digital Twin for Qualification and Certification of Metals Additive Manufacturing
Towards a Probabilitic Model for the Assessment of Gas Turbine Components
Transitioning from Basic Research to Industrial Applications for Metal AM Components
Uncertainty Quantification and Sensitivity Analysis in Process-Structure-Property Simulations for Laser Powder Bed Fusion Additive Manufacturing
Uncertainty Quantification in Process-Structure-Property Dynamics of IN718
Using Unsupervised Learning to Cluster Fatigue Life Based on Ti64 Fatigue Fracture Surface Characteristics

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