Conference Logo ProgramMaster Logo
Conference Tools for MS&T26: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract

Meeting MS&T26: Materials Science & Technology
Symposium Computational Materials for Qualification and Certification
Presentation Title Providing Validation Datasets for Materials Process Modelling: A Cornell High Energy Synchrotron Source Perspective
Author(s) Amlan Das, Kelly Nygren, Arthur Woll, Matt Miller, Paul Shade
On-Site Speaker (Planned) Amlan Das
Abstract Scope Process and performance modeling are essential tools for accelerating materials manufacturing, qualification, and deployment. The validation of these tools is often limited by the lack of ground-truth experimental data needed to anchor them. Modern x-ray synchrotron sources provide a unique opportunity to generate high-throughput, high-fidelity measurements that enable validation of process-structure-property-performance relationships across relevant length and time scales. Simultaneously, the materials community faces a critical need to mature advanced characterization techniques through robust workflows, uncertainty quantification, and standardized analysis approaches. This work highlights CM4QC-focused developments at Cornell High Energy Synchrotron Source, including component-scale studies, in-operando process simulations, and autonomous experimentation workflows. We also present the multi-funding partner structural materials program at CHESS and its mission-driven framework, which creates unique opportunities not only for advancing synchrotron experimental technique development, but also for developing the reproducibility, standards, uncertainty-aware workflows, and industrial accessibility needed to transition advanced characterization toward broader engineering adoption.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Characterization of Microstructure in Printed Alloys Prone to Solidification Cracking
Acoustics for In-Situ Process Data to Inform Computational Models in Metal Additive Manufacturing
AI-Driven Materials Design for Resilient Manufacturing Under Uncertainty
An Overview of the OPAL Digital Twin to Predict Fatigue Lives Via a Inputs from Computational Materials Models and In-Situ Sensing
Benchmarking Spectral Solution Methods for the Mechanical Behavior of Additively Manufactured Metals Containing Pores
Challenges in Materials Maturation for Additive Manufacturing
Computational Materials for Qualification and Certification Steering Group and Community Vision Roadmap
Establishing the Severity of Pores in Structural Components
Integrated Modeling of Solidification Cracking in Fusion Welding of High-Strength Aluminum: Multi-Criteria Analysis Under Variable Restraints
Metal Additive Manufacturing Simulations Driven by In-Situ Experimental Data for Qualification and Certification
Practical Data Management in Computational Materials for Qualification and Certification
Probabilistic Fatigue Modeling of Powder Bed Fusion – Laser Beam Ti-6Al-4V with Model- and Measurement-Based Uncertainty
Providing Validation Datasets for Materials Process Modelling: A Cornell High Energy Synchrotron Source Perspective
Qualification and Certification for Additive Manufacturing Parts in the US Navy
Robust Manufacturing and Qualification of 3D-Printed Ceramics
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
Towards a Predictive Modeling Platform for Fatigue in Additively Manufactured Metals
Transitioning from Basic Research to Industrial Applications for Metal AM Components

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