Senvol, ORNL Team for Report on Pedigree AM Data

February 19, 2021


Senvol and the U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) have published a downloadable technical report, Collection of High Pedigree AM Data for Data Analysis and Correlation, with report findings stemming from a 2-yr. cooperative R&D agreement focused on pedigreed additive manufacturing (AM) data generation.

“Pedigree,” in this sense, refers to a history of “appropriate geometric information, key processing parameters for the AM technology and any key material testing protocols,” says Peeyush Nandwana, AM and powder-metals researcher at ORNL, in describing the project. Information in the report, which includes analysis of the resulting yield strength, elongation and ultimate tensile strength of Al-10Si-Mg alloy fabricated via laser powder bed fusion, may be helpful particularly in developing process parameters for new materials and machines, according to project principals.

“The impact of this project will be significant in helping the AM industry understand the necessity of producing pedigree data,” says Ryan Dehoff, secure and digital manufacturing lead at ORNL. “We’ve demonstrated that pedigree data collection is critical to understanding the quality of AM materials, and ensured that all of the nuanced data required to accurately extract information is captured.”

Senvol worked with ORNL to evaluate and implement Senvol’s proprietary Standard Operating Procedure (SOP) document for collection of pedigree data for AM using a laser powder-bed fusion machine with the Al-Si-Mg alloy. ORNL independently evaluated and provided feedback to Senvol regarding the SOP document. This feedback was incorporated in the document and employed to fabricate builds on a Concept Laser XLine 1000r AM machine to evaluate the efficacy of the document in collecting pedigreed data. The builds, performed via varying build parameters, produced samples then subjected to tensile testing. The tensile data served as an input for Senvol’s machine learning software, Senvol ML, to determine the correlation between the build parameters and resulting tensile strength.

“Collectively, we were able to show that generating the data at the scale in this work and leveraging the use of correlation functions from Senvol’s machine learning software, Senvol ML, can provide the basis for isolating the impacts of different variables on resulting material properties and performance,” explains Annie Wang, Senvol president. 

Industry-Related Terms: Additive manufacturing, Powder bed
View Glossary of 3D Metal Printing Terms

 

See also: Senvol

Technologies: Metal Powders, Software

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