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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
Presentation Title Challenges in Producing, Curating, and Sharing Large Multimodal, Multi-institutional Data Sets for Additive Manufacturing
Author(s) Lyle E. Levine, Brandon Lane, Carelyn E. Campbell, Gerard Lemson , Edwin J. Schwalbach, Megna Shah
On-Site Speaker (Planned) Lyle E. Levine
Abstract Scope The additive manufacturing benchmark series (AM Bench) provides the AM community with rigorous measurement datasets for model validation that are permanently archived and freely available. In addition, challenge problems are posed to the modeling community to evaluate the state-of-the-art for AM simulation. Planning and executing these measurements pose numerous challenges but developing the necessary data management and data sharing systems are equally important. Questions to be addressed include: How do data collection and sharing challenges impact the benchmark choices? How do we track samples using persistent identifiers? How can we curate the data and metadata and enable users to explore terabyte-sized, multimodal data sets? How do data choices affect communication with challenge problem participants and evaluation of their simulation results? Although workable solutions to these and other questions and challenges have been developed, work continues on improved solutions that are easy to use and maintain.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Quest for Re-using 3D Materials Data
A Validation Framework for Microstructure-sensitive Fatigue Simulation Models
Added Value and Increased Organization: Capturing Experimental Data Provenance in Materials Commons 2.0
Challenges in Producing, Curating, and Sharing Large Multimodal, Multi-institutional Data Sets for Additive Manufacturing
Data-driven Model Based Comparison of Public Datasets for Online State of Charge Estimation in Lithium-ion Batteries
Filling Data Gaps in 3D Microstructure with Deep Learning
Generating, Sharing, and Using Halide Perovskite Exploratory Synthesis Data to Discover New Materials
Graph Convolutional Neural Networks for Fast, Accurate Prediction of Material Properties for Solid Solution High Entropy Alloys Using Open-source Datasets
Holistic Merging of Experimental and Computational Datasets – A Case Study for Diffusion Coefficients
Materials Innovation and Design Enabled by the Materials Project
Mg Database Project: Mapping Trends and Data Sets of Magnesium and Its Alloys for Improved Mechanical Performance
NOW ON-DEMAND ONLY - Hard Fought Lessons on Open Data and Code Sharing and the Terra Infirma of Ground Truth
The Status of ML Algorithms for Structure-property Relationships Using Matbench as a Test Protocol

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