Senvol is developing data-driven machine learning additive manufacturing (AM) software for the U.S. Navy's Office of Naval Research (ONR). Senvol's software analyzes the relationships between AM process parameters and material performance.
ONR's goal is to use Senvol's software to assist in developing statistically substantiated material properties in hopes of reducing conventional material characterization and testing that is needed to develop design allowables.
Senvol President Annie Wang commented, "We are very excited about our work with the Navy's Office of Naval Research. 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."
Wang continued, "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."
A modularized ICME (integrated computational materials engineering) probabilistic framework for AM data serves as the foundation for Senvol's software. In this framework, AM data is categorized into four modules: Process parameters, process signatures, material properties, and mechanical performance. The software being developed is powered by an algorithm that quantifies the relationships between the four modules. The algorithm is AM material, machine, and process agnostic.
The software under development will be made commercially available to any company looking to qualify AM parts. If your company is interested in potentially gaining beta access to the software, you can contact Senvol for more details.
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