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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
Presentation Title Data Management in Additive Manufacturing – Lessons Learned and Opportunities
Author(s) Mahdi Jamshid, Mohsen Seifi, David Eduardo E. Paredes
On-Site Speaker (Planned) David Eduardo E. Paredes
Abstract Scope While AM is just “another tool in the toolbox”, or another way of making parts, it offers a unique opportunity which is the ability to collect large amounts of data from every single layer during the build process. Such a data-rich process should in theory allow users to develop simulation or machine learning packages to better understand, optimize, or customize the technology. However, this objective hasn’t been fully materialized due to various reasons including data quantity and quality. This presentation will provide an overview of the Data Management activities at the ASTM Advanced Manufacturing Center of Excellence (AM CoE). Key process variables (KPVs), automated data acquisition, data quality assessment, data transfer and sharing, as well as standardization gaps are among topics that will be discussed. In addition, our experiences, in collaboration with multiple organizations through government- and consortium-funded projects and lessons learned will be shared with the audience.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing,


A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems
Advanced Mechanical Properties Prediction of Functionally Graded Materials through High-Throughput Characterization.
Advances in Atom Probe Crystallographic Analysis
Connectivity of Experimental Equipment and Interoperability of Experimental Data: Challenges and Opportunities
Data-driven Discovery of Dynamics from Time-resolved Coherent Scattering
Data Management in Additive Manufacturing – Lessons Learned and Opportunities
Data Management, Data Sharing and the Future of Federal Research Funding
Deep Learning-Driven Semantic Segmentation of large 4D Lab-Scale X-ray Tomography Data for Quantification of Microstructural Features
Directional Reflectance Microscopy: Beyond Conventional Crystal Orientation Mapping
Enabling Uninterrupted In-situ X-ray Experiments through Rapid Data Feedback and On-the-fly Experiment Optimization
G-19: Accessing the Microstructure State Space
G-20: TESCAN TENSOR a 4D-STEM for Multimodal Characterization of Challenging and Interesting Specimens
Galaxy: A Critical Framework for Large Data Volumes and Data-intensive Processing in the Synchrotron World
Hierarchical Bayesian Data Analysis for Accelerating Structural Materials Characterization
HPC+AI@Edge Enabled Real-Time Materials Characterization
Melt Pool Quantification from In Situ Radiography of Directed Energy Deposition of Nickel Superalloys
New Strong and Ductile Titanium-oxygen-iron Alloys Enabled by AM and Insights from Multiscale Microscopy
Probabilistic Orientation Analysis via Direct ODF Calculation from Far Field HEDM
Quantitative 2D and 3D Characterization of Precipitates Microstructure in the Additively Manufactured Titanium Alloy
Real-Time In-Situ Characterization with Web Technologies at Any Scale
Streamlining Engineering Diffraction Analysis Using the MAUD Interface Language Kit (MILK)
Understanding Relaxation Dynamics Beyond Equilibrium Using AI-Informed X-ray Photon Correlation Spectroscopy
Using Video Games for Training Data on Microstructural Design
Utilizing Advanced Computer Vision Techniques Based on Machine Learning and Artificial Neural Networks to Process Micrographs of Ni-base Superalloys
Utilizing Deep Learning Techniques to Accelerate X-ray Absorption and Diffraction Contrast Imaging

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