Performance Prediction and Process Attribution for Additive Manufacturing
EMSL Project ID
50108
Abstract
The proposed work seeks to develop a modeling-prescribed measurement strategy to generate the data necessary to build a finite element model from which an additively manufactured objects’ performance can be predicted. Data analytics will be used in parallel to identify notable feature characteristics (from production all the way through to end-use) that can be directly correlated as signatures to specific processing conditions. To generate the data set needed for this undertaking, AM standards will be prepared under tightly controlled conditions, with all input variables being recorded throughout the fabrication process. Once these standards are thoroughly validated, conditioning experiments that reflect “in-service” scenarios will be performed. In this way, factors related to aging, such as photo-degradation and hydrolysis, can be incorporated into performance predictions and provenance determination.
Project Details
Start Date
2017-11-10
End Date
2018-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members
Related Publications
Kennedy Z.C., D.E. Stephenson, J.F. Christ, T.R. Pope, B.W. Arey, C.A. Barrett, and M.G. Warner. 2017. "Enhanced anti-counterfeiting measures for additive manufacturing: coupling lanthanide nanomaterial chemical signatures with blockchain technology." Journal of Materials Chemistry C 5, no. 37:9570-9578. PNNL-SA-127746. doi:10.1039/C7TC03348F
Kennedy Z.C., J.F. Christ, K.A. Evans, B.W. Arey, L.E. Sweet, M.G. Warner, and R.L. Erikson, et al. 2017. "3D-Printed Poly(vinylidene fluoride) / Carbon Nanotube Composites as a Tunable, Low-Cost Chemical Vapour Sensing Platform." Nanoscale 9, no. 17:5458-5466. PNNL-SA-123459. doi:10.1039/C7NR00617A