Machine learning software Senvol ML™ used to ensure part performance across different 3D printers

Software company Senvol 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 (AM) machines located at different sites.

The goal of the program is to accommodate the reduction of cost and lead time of Army ground vehicle systems while increasing the performance. Senvol will utilize its AM machine learning software, Senvol ML, to reduce the cost of material and process development and to enable the Army to produce AM parts of consistent performance even when using different AM machines located at different sites (potentially using different process parameter sets).

The program, titled “Applying Machine Learning to Ensure Consistency and Verification of Additive Manufacturing (AM) Machine and Part Performance Across Multiple Sites” commenced in March 2023 and will run through March 2025.

Aaron LaLonde, PhD, Technical Specialist – Additive Manufacturing at the U.S. Army stated, “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.”

The approach demonstrated in the program will be able to be applied to any AM process, any AM material, and any AM machine. Senvol will also develop and validate an approach that can be used to continue to verify AM machine and part performance when there are changes to the process (e.g., when a new powder supplier is used).

During the program, Senvol will use Senvol ML to rapidly develop process parameters and establish a process model for different AM machines located at different sites. Senvol ML will be used to quantify complex and interdependent PSPP relationships.

Senvol President Zach Simkin commented, “Consistency – or a lack thereof – is a problem that nearly everyone in the additive manufacturing industry can relate to. The Army, and DoD in general, has been at the forefront of tackling pressing issues in our industry, and we are pleased to work with them again to demonstrate the use of our machine learning software as a mechanism to ensure consistent part performance across different sites and machines.”

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