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Integrating Materials and Manufacturing Innovation
Published in: Integrating Materials and Manufacturing Innovation 4/2022

31-10-2022 | Technical Article

A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems

Authors: Rohan Casukhela, Sriram Vijayan, Joerg R. Jinschek, Stephen R. Niezgoda

Published in: Integrating Materials and Manufacturing Innovation | Issue 4/2022

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Abstract

Autonomous experimentation systems have been used to greatly advance the Integrated Computational Materials Engineering paradigm. This paper outlines a framework that enables the design and selection of data collection workflows for autonomous experimentation systems. 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. We employ this framework to select the optimal high-throughput workflow for the characterization of an additively manufactured Ti–6Al–4V sample using a deep-learning based image denoiser. The selected workflow reduced the collection time of backscattered electron scanning electron microscopy images by a factor of 5 times as compared to the case study’s benchmark workflow, and by a factor of 85 times as compared to the workflow used in a previously published study.
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Metadata
Title
A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems
Authors
Rohan Casukhela
Sriram Vijayan
Joerg R. Jinschek
Stephen R. Niezgoda
Publication date
31-10-2022
Publisher
Springer International Publishing
Published in
Integrating Materials and Manufacturing Innovation / Issue 4/2022
Print ISSN: 2193-9764
Electronic ISSN: 2193-9772
DOI
https://doi.org/10.1007/s40192-022-00280-5

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