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Senvol Demonstrates Machine Learning Approach to Material Allowables
Senvol recently demonstrated a machine learning approach to material allowables development that was shown to be more flexible, cost-effective, time-effective, and equivalent to the conventional (in this case, MMPDS) approach to material allowables development.
As part of the program, Senvol demonstrated a new approach to material allowables development that leverages machine learning. A machine learning approach is extremely flexible and able to handle any change to the AM process, which makes this approach ideal for sustainment in the long-term. The program focused on demonstrating the approach using a 17-4 PH Stainless Steel material processed via a powder bed fusion AM machine.
🖨️ Senvol to lead U.S. Army program focused on consistency of 3D printing performance
Senvol has announced that it has received funding from the U.S. Army to lead a program focused on demonstrating that consistent part performance can be achieved on different additive manufacturing machines located at different sites.
The program is titled “Applying Machine Learning to Ensure Consistency and Verification of Additive Manufacturing Machine and Part Performance Across Multiple Sites”, and commenced in March 2023, running through March 2025.
Aaron LaLonde, PhD, Technical Specialist – Additive Manufacturing at the U.S. Navy said “For additive manufacturing to be successfully implemented into the Army’s supply chain, it is essential to be able to produce parts of consistent performance even if different machines are used at different locations. Today, that is much easier said than done. During this program, we are pleased to work with Senvol to demonstrate the use of its machine learning technology to aid in achieving what everyone in the additive manufacturing industry strives for, a truly flexible supply chain.”