Qualification of new 3D printing materials is often mentioned as one of the tedious challenges that hinder the adoption of AM technologies. Depending on the industry or the application, AM companies continuously explore new ways to optimize their qualification.
A good example has been given by software company Senvol today, which leverages a machine learning approach to material property allowables development. Under a contract awarded by US AM accelerator America Makes, the company has deployed its Senvol ML software to rapidly and cost-effectively identify property allowables during material R&D. The company that received more funding last year can now demonsrate how it further advances its technology through this project.
Senvol’s partners on the program included Northrop Grumman, the National Institute for Aviation Research (NIAR), Stratasys Direct Manufacturing, and Pilgrim Consulting.
In aerospace AM especially, it’s often hard to completely achieve the benefits of AM in terms of lightweight and complex designs because of 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 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 inevitably changes to the AM process.
Key advantages in leveraging a machine learning approach
According to Senvol, this machine learning approach would just as accurate as the conventional (in this case, CMH-17) approach to allowables development. One of the advantages here is the ability to handle any chane to the AM process which allows 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.
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 allowables. 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 comments, “Senvol implemented data-driven machine learning technology that has the potential to substantially reduce the cost of material allowables development. By demonstrating an entirely new – and significantly more efficient – approach to allowables development, Senvol aims to drive tremendous value for the U.S. Air Force, the America Makes membership, and the additive manufacturing industry at large.”
Simkin continues, “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.”
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, adds, “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 allowables. 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.”
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