“We are very excited about our work with the Navy’s Office of Naval Research,” said Senvol President Annie Wang. “Our software’s capabilities will allow ONR to select the appropriate process parameters on a particular additive manufacturing machine given a target mechanical performance. This presents a unique opportunity to reduce the high level of trial and error that is currently required, which would save a tremendous amount of time and money.
In addition to our machine learning capabilities, we have also developed a computer vision algorithm that analyzes, in real-time, in-situ monitoring data. This enables us to detect irregularities in real-time and begin to quantify the relationships between irregularities in the build and the resulting mechanical performance.”
Senvol’s software is built on a modularized integrated computational materials engineering (ICME) probabilistic framework for additive manufacturing data. Within that framework, additive manufacturing data is categorized into four modules: process parameters, process signatures, material properties and mechanical performance. The software is powered by an algorithm that quantifies the relationships between the four modules; the algorithm is additive manufacturing material, machine and process agnostic.
The software is being funded through Navy Phase II STTR N16A-002. It is still under development, but it will be made available to any company looking to qualify its additively manufactured parts. If your company would like to possibly obtain beta access to the software, you can contact Senvol at info@senvol.com.
If you’re interested in learning more about the software under development, Senvol will be presenting its work at several upcoming conferences, including AMUG (April 8-12), RAPID + TCT (April 23-26), and CAASE18 (June 5-7).
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