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Feedstock-Structure-Property Relationships in the Additive Manufacturing of High-Performance Thermoplastics


EMSL Project ID
51797

Abstract

As part of the Chemical Dynamics Initiative at PNNL, this project and use case aim to establish testbeds to 1) create data-driven performance models for AM-produced components with uncertainties well understood; 2) streamline the correlation of material or process signatures characteristic of AM processes for provenance, increased fundamental understanding, and to support QA/QC in the field. Examples of specific areas of focus include: printing structures with high-performance polymers and related blends or composites on low-cost machines; data assimilation for microstructure prediction by coupling models and in situ experiments; evolution of micro- and -chemical structure present in printed parts due to post-processing and during application in harsh real-world environments; the use of data and workflow management infrastructure to accelerate analysis; and application of deep learning tools on large data sets collected for classification and property prediction.

Project Details

Start Date
2020-12-18
End Date
2021-09-30
Status
Closed

Team

Principal Investigator

Zachary Kennedy
Institution
Pacific Northwest National Laboratory

Team Members

Anil Krishna Battu
Institution
Environmental Molecular Sciences Laboratory

Katherine Koh
Institution
Pacific Northwest National Laboratory

Tamas Varga
Institution
Environmental Molecular Sciences Laboratory

Satish Nune
Institution
Pacific Northwest National Laboratory