Feedstock-Structure-Property Relationships in the Additive Manufacturing of High-Performance Thermoplastics
EMSL Project ID
51490
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
2020-05-01
End Date
2020-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members