Machine-Learning Software Eases Material-Allowables Development
February 22, 2022Comments
Through a contract awarded by America Makes and funded by the U.S. Air Force, Senvol has applied its machine-learning (ML) software, Senvol ML, to enable a path to rapid development of material property allowables for additive manufacturing (AM). The approach was shown to be more flexible, more cost-effective, more time-effective and just as accurate as a conventional approach to allowables development, according to Senvol officials. An ML 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 officials continue.
The America Makes-Air Force program focused on demonstrating the approach using a Nylon 11 flame-retardant material processed via a polymer powder-bed fusion AM machine—it has applications for metal and nonmetal AM. As AM has started to enable lightweight and rapidly produced designs that are revolutionary to various U.S. Air Force and commercial capabilities and applications, these benefits reportedly cannot yet be fully realized due to the time and high cost of allowables development.
The high cost stems in large part from the fact that material allowable development requires an enormous amount of empirical data to be generated, at a fixed processing point, meaning that all of the empirical data must typically be regenerated from scratch for each major change in the process. This results in an AM process that is not only costly and time-consuming to implement the first time, claim Senvol officials, but costly and time-consuming to maintain in the long run when changes to the AM process inevitably occur.
Senvol ML software supports the qualification of AM processes and was used in the program to develop statistically substantiated material properties analogous to material allowables. Furthermore, it did so while simultaneously optimizing data generation requirements. The flexible software reportedly can be applied to any AM process, any AM machine, and any AM material.
