Peeyush Nandwana, researcher in powder metals and additive manufacturing at ORNL, added, “This SOP covers topics such as collecting appropriate geometric information, key processing parameters for the AM technology, and any key material testing protocols. 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.”

Ryan Dehoff, secure and digital manufacturing lead at ORNL, stated, “The impact of this project will be significant in helping the additive manufacturing industry understand the necessity of producing pedigree data. 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 builds were done with varying build parameters, and the samples were subjected to tensile testing. 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. ORNL independently evaluated and provided feedback to Senvol regarding the SOP document. 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. The findings of the report stem from a two-year cooperative research and development agreement focused on pedigreed additive manufacturing (AM) data generation. 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.”

Concept laser xline 1000r 3d printer