| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
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| Presentation Title |
A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems |
| Author(s) |
Stephen R. Niezgoda, Rohan Casukhela, Sriram Vijayan, Joerg Jinschek |
| On-Site Speaker (Planned) |
Stephen R. Niezgoda |
| Abstract Scope |
Autonomous experimentation has been used to advance the ICME paradigm. This talk outlines a framework that enables the design and selection of data collection workflows for autonomous experimentation systems. The development begins from fundamental principles: All data collection efforts must begin with specifying an objective that needs to be met. A well-designed 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 generates relevant 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 that adds significant 𝐕𝐚𝐥𝐮𝐞 to the broader objective. We use the direct product of the workflow, the extracted information, as a measure of the value of the workflow itself. The 𝐕𝐚𝐥𝐮𝐞 of information is proportional to the information’s 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 and 𝐀𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲. The framework first searches for data collection workflows that generate high-quality information and then selects the workflow that generates the highest-value information as per a user-defined objective. This talk will outline the framework and demonstrate applicate to optimal selection of a high-throughput workflow for the characterization of an additively manufactured Ti–6Al–4V. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
ICME, Computational Materials Science & Engineering, Other |