Senvol recently demonstrated a machine learning approach to material property allowable development that was shown to be more flexible, more cost-effective, more time-effective, and just as accurate as the conventional (in this case, CMH-17) approach to allowable development. The work was done as part of a contract that Senvol had been awarded by America Makes, the national additive manufacturing institute, and funded by the U.S. Air Force, to apply its machine learning software, Senvol ML, to enable a path to the rapid development of material property allowable for additive manufacturing (AM). Senvol’s partners on the program included Northrop Grumman, the National Institute for Aviation Research (NIAR), Stratasys Direct Manufacturing, and Pilgrim Consulting.
As part of the program, Senvol demonstrated a new approach to material property allowable 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 Nylon 11 Flame Retardant material processed via a polymer powder bed fusion AM machine.
“Additive manufacturing is a modern and digital manufacturing method with rapidly tailorable processing. To continue to use traditional material allowable development approaches is a bottleneck to wider material and process options, and capabilities for additive manufacturing,” said Dr. Brandon Ribic, America Makes Technology Director. “Senvol’s program was very powerful in demonstrating an approach to additive manufacturing allowable that leverages the digital nature of the technology and leverages machine learning, a modern data analysis approach that has been shown to be extremely effective in a multitude of other industries.”
AM is starting to enable lightweight and rapidly produced designs that are revolutionary to various U.S. Air Force and commercial capabilities and applications. These benefits cannot yet be fully realized due to the time and high cost of allowable 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 every time there is a major change in the process. This results in an AM process that is not only costly and time-consuming to implement the first time, but costly and time-consuming to maintain in the long run when there are inevitable changes to the AM process.
The Senvol ML software supports the qualification of AM processes and was used in the program to develop statistically substantiated material properties analogous to material allowable. Furthermore, it did so while simultaneously optimizing data generation requirements. Important to note is that the software is flexible and can be applied to any AM process, any AM machine, and any AM material.
Senvol President Zach Simkin commented, “Senvol implemented data-driven machine learning technology that has the potential to substantially reduce the cost of material allowable development. By demonstrating an entirely new – and significantly more efficient – approach to allowable development, Senvol aims to drive tremendous value for the U.S. Air Force, America Makes membership, and the additive manufacturing industry at large.” Simkin continued: “The results of this America Makes program were incredibly successful. Additionally, we identified several other opportunity areas to go deeper into the machine learning capabilities to address this critical need for the industry. We look forward to continuing to partner with industry to advance this cutting edge area.”
Users of the Senvol ML software include organizations in aerospace, defense, oil & gas, consumer products, medical, and automotive industries, as well as AM machine manufacturers and AM material suppliers. The results from this America Makes program are available to America Makes membership as well as to the U.S. Government.
Dr. William E. Frazier, retired Chief Scientist for Air Vehicle Engineer at NAVAIR / The Navy Senior Scientist for Material Engineering, and currently, President of Pilgrim Consulting LLC, added “I was very pleased to join Senvol’s team for this program. Senvol’s machine learning-enabled approach directly addresses a major industry challenge: the rapid and cost-effective development of additive manufacturing material property allowable. I have been involved with the qualification of several additive manufacturing processes and materials for flight, and in my opinion, the further development of this technology will have a positive impact on the cost, schedule, and performance of both DoD and commercial platforms.”