Framingham, MA – September 24, 2018 – Digital Engineering magazine, which is the primary destination for high-performance computing and simulation-based modeling information, published an article today titled “Senvol and NIST: New Project to Establish AM Process-Structure-Property Relationships.”
The article discusses Senvol’s recent project with NIST, which is titled, “Continuous Learning for Additive Manufacturing Processes Through Advanced Data Analytics.” For the project, Senvol will utilize its data-driven machine learning software for additive manufacturing (AM), Senvol ML, to establish process-structure-property relationships for AM.
Digital Engineering Contributing Editor Pamela J. Waterman writes:
“The top-level project goal is to apply data analytics to both empirical and physics-based AM data to establish process-structure-property (PSP) relationships…. Senvol will analyze the combined data with its Senvol ML analysis software, applying the resulting ‘learned’ knowledge to predicting the material properties and performance of new metal parts based on proposed process parameters.”
Waterman continues: “the big benefit of the data-driven approach is that it can be applied to any machine, any material or any process; the Senvol ML software also runs very quickly on any computer. By contrast, a physics-based approach needs a specific model for each specific AM process; those models can take years to build and even more years to validate plus, in some cases, days of supercomputer run-time when used.”
To read the full article, click here.
To learn more about Senvol ML, click here.
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