Developing material property allowables for AM

3D printing software specialist Senvol has been awarded a contract by America Makes and the US Air Force to use its machine learning software to develop material property allowables for additive manufacturing (AM).

A material design allowable is a material property such as compressive yield strength, while a design allowable takes into account properties such as structure geometry.

The company says that a machine learning approach was more flexible, cost-effective, time-effective, and as accurate as conventional approaches. In this case, the program focused on demonstrating the approach using a Nylon 11 flame retardant material processed via a polymer powder bed fusion AM machine.

According to the company, a machine learning approach is extremely flexible and able to handle any change to the AM process, which makes this approach more suitable for sustainment in the long-term.

“To continue to use traditional material allowables development approaches is a bottleneck to wider material and process options, and capabilities for AM,” said Dr Brandon Ribic, America Makes technology director. “Senvol’s program was very powerful in demonstrating an approach to additive manufacturing allowables 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.”

Allowables development can be costly because an enormous amount of empirical data has to be generated, at a fixed processing point, so 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, Senvol said.

The company’s 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.

“By demonstrating an entirely new – and significantly more efficient – approach to allowables development, Senvol aims to drive tremendous value for the US Air Force, the America Makes membership, and the additive manufacturing industry at large,” said Senvol president Zach Simkin.

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