It’s somewhat of an understatement to say that a lot of 3D printing research takes place at Oak Ridge National Laboratory (ORNL). The facility is constantly making headlines for its groundbreaking work, much of it involving 3D printing in some form. The 3D printing work that takes place there is almost always for highly precise, important applications, and so strict quality control and documentation procedures are needed. Now ORNL is using its expertise to help improve those procedures universally, with some help from additive manufacturing expert Senvol.
Senvol helps companies better implement additive manufacturing through three tools: the Senvol Database, the Senvol API and the Senvol Indexes. It’s hard to find a better resource, as the Senvol Database contains comprehensive data on existing 3D printers and materials. Cool company Senvol has now signed a two-year research agreement with ORNL for a project focused on pedigreed additive manufacturing data generation.
The project will evaluate best practices for data collection, using Senvol’s proprietary Standard Operating Procedure (SOP) document for generating pedigreed additive manufacturing data. ORNL and Senvol will work together to investigate pedigreed data collection to better understand the quality of additive manufacturing materials and to ensure that all required nuanced data is captured and accurately extracted during an additive manufacturing data generation project.
“Senvol has been at the forefront of pedigreed data for additive manufacturing,” said Ryan Dehoff, Group Leader of the Deposition Sciences and Technology Group at ORNL. “The importance of understanding the relationship between material properties, machine selection, and process parameters is critical for helping industry move from prototypes to industrial parts.”
Topics covered by Senvol’s SOP include the collection of appropriate geometric information, key processing parameters for additive manufacturing technology, and key material testing protocols. These topics are important for understanding true material response, especially when using multivariate analysis approaches in which several variables may be interlinked.
“Oak Ridge is renowned for having world-class expertise in additive manufacturing, and so we’re very excited to work with them on this project,” said Senvol President Annie Wang. “The pedigreed data generated during the project will be input into Senvol’s data structure in order to perform preliminary machine learning and data analysis. Senvol is currently evaluating and building advanced computational tools to rapidly evaluate AM components and link processing, microstructure, and properties in additively manufactured components. The results of this project will be used to complement physics based models of additive manufacturing systems and therefore lead to more rapid understanding of new materials and faster deployment of the technology.”
The Senvol Database was launched in 2015, with the Senvol API and Senvol Indexes introduced shortly after that. Despite the fact that it’s only been around for a couple of years, the site contains a vast amount of additive manufacturing information, and the collaboration with ORNL will only allow it to expand more.
The research project between Senvol and ORNL is being supported by the Department of Energy’s Office of Energy Efficiency and Renewable Energy – Advanced Manufacturing Office under the Manufacturing Demonstration Facility at ORNL.
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