Towards the Development of a Multi-Modal Community-Based AM Database

Casukhela, Rohan; Vijayan, Sriram; Jacobsen, Matthew; Fourman, James; Hepler, Kayla; Shao, Meiyue; Gupta, Avantika; Jinschek, Joerg R.

doi:10.1017/s1431927621004104

Cited by 4 publications

(1 citation statement)

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“…However, increased Accuracy and the #Sources typically leads to increased Cost of acquisition. This is due to the extra time needed and the extra resources required to collect, structure and curate the data [ [25]].…”

Section: Workflow Design Frameworkmentioning

confidence: 99%

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

Casukhela¹,

Vijayan²,

Jinschek³

et al. 2022

Preprint

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Autonomous experimentation systems have been used to greatly advance the integrated computational materials engineering (ICME) 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 best, highest-value information as per a userdefined objective. We employ this framework to select the user-defined best high-throughput workflow for material characterization on an additively manufactured Ti-6Al-4V sample for the purposes of outlining a basic materials characterization scenario, reducing the collection time of backscattered electron scanning electron scanning electron microscopy images by a factor of 5 times as compared to the benchmark workflow for the case study presented, and by a factor of 85 times as compared to the workflow used in the previously published study.

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Section: Workflow Design Frameworkmentioning

confidence: 99%

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

Casukhela¹,

Vijayan²,

Jinschek³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Toward a Standard Data Architecture for Additive Manufacturing

Li,

Feng,

Kuan

et al. 2024

JOM

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To advance additive manufacturing (AM), a scalable architecture is needed to structure, curate and access the data from AM R&D projects that are conducted to evaluate new materials, processes and technologies. Effective project metadata management enables the sharing of AM domain knowledge. This work introduces an AM data modeling architecture to capture pedigree information from AM projects which enables the traceability of the material. This overall AM model includes five modules covering information about (1) project management, (2) feedstock materials, (3) AM building and post processing, (4) microstructure and properties measurements and (5) computer simulations. The objective of this design is to ease the integration of the heterogeneous datasets from different sources and allow for extensions, for example, to incorporate sub-models from other efforts. As a proof of concept, the material and process models defined in the paper capture the major metadata elements for laser powder bed fusion AM. To demonstrate the effectiveness of the architecture, the models are implemented using extensible markup language and preliminarily tested using the project data from America Makes. Additional data sub-models can be integrated in this architecture without affecting the existing structure.

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