Senvol and Oak Ridge National Laboratory Publish Additive Report

Technical report is titled “Collection of High Pedigree AM Data for Data Analysis and Correlation.”

Technical report is titled “Collection of High Pedigree AM Data for Data Analysis and Correlation.”

Image courtesy of Senvol and Oak Ridge National Laboratory.


Senvol and the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) have published a technical report titled “Collection of High Pedigree AM Data for Data Analysis and Correlation.”

The findings of the report stem from a 2-year cooperative research and development agreement focused on pedigreed additive manufacturing (AM) data generation.
 
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 an Al-Si-Mg alloy.

ORNL independently evaluated and provided feedback to Senvol regarding the SOP document. Those edits were incorporated in the document that was then used to fabricate builds on a Concept Laser XLine 1000r to evaluate the efficacy of the document in collecting pedigreed data. The builds were done with varying build parameters, and the samples were subjected to tensile testing. The tensile data was used as an input for Senvol’s machine learning software, Senvol ML, to determine the correlation between the build parameters and resulting tensile strength.

“The impact of this project will be significant in helping the additive manufacturing 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 additive manufacturing materials, and ensured that all of the nuanced data required to accurately extract information is captured.”

“This SOP covers topics such as collecting appropriate geometric information, key processing parameters for the AM technology, and any key material testing protocols,” says Peeyush Nandwana, researcher in powder metals and additive manufacturing at ORNL. “These are critical in terms of understanding the true material response, especially when dealing with multivariate analysis approaches in which several of these variables may be interlinked.”

“Oak Ridge National Laboratory has distinguished expertise in additive manufacturing, and so we were very pleased to work with them on this project,” Senvol President Annie Wang comments. “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. This can be particularly helpful in developing process parameters for new materials and machines.”

To learn more about Senvol SOP, Senvol's standard operating procedure for generating pedigreed AM data, click here.
 
To learn more about Senvol ML, Senvol’s data-driven machine learning software for AM, click here. 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

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