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Senvol’s ML software to be applied by the US Army Research Lab for 3D printing missile parts 

The US Army Research Laboratory (ARL) has awarded a contract to Senvol that will see its Machine Learning (ML) software used to help design and qualify 3D printed missile parts. 

As part of the program, Senvol is set to leverage its ML algorithms to develop a flexible ‘qualification plan,’ that can be applied to any 3D printing method, component or machine. According to Annie Wang, President of Senvol, deploying the firm’s ML technology will enable the Army to reduce part lead times and costs, while increasing soldier survivability. 

“Senvol will implement data-driven ML technology for the U.S. Army that will substantially reduce the cost of material and part qualification,” said Annie Wang, President of Senvol. “The significant increase in speed will allow the Army to support warfighter readiness by unlocking the full transformative potential that AM offers.”

The ARL is aiming to use Senvol's ML algorithm to develop a method for qualifying 3D printed missile parts. Photo via the ARL.
The ARL is aiming to use Senvol’s ML algorithm to develop a universal method for qualifying 3D printed missile parts. Photo via the ARL.

The military applications of ML algorithms 

Senvol is a New York-based software developer that specializes in providing businesses with sufficient data to incorporate 3D printing into their design and production processes. The company initially established an AM database with this goal in mind in 2015, and has since expanded its offering to include a suite of API, Indexes, SOP and ML digital products. 

Using its proprietary ML algorithm, Senvol is able to quickly establish the relationship between a printer’s parameters and any resulting parts’ performance. The program works by calculating the optimal properties of a given material for its users, enabling them to reduce any time spent on generating design allowables. 

Although Senvol’s software has been deployed in industries ranging from automotive to the medical sector, it has been particularly effective within military applications. For instance, the U.S. Navy’s Office of Naval Research (ONR) has utilized the ML program to reduce the lead times associated with seafaring components.

Recently, the U.S. Air Force has also partnered with Senvol to develop baseline mechanical properties for its PBF EOS 3D printers. Having been awarded a contract by the ARL, the company will now attempt to identify a more universal qualification methodology, with the end-goal of designing enhanced fabricated missile parts. 

The US Air Force is using Senvol's AM software to optimize the settings of EOS 3D printers for producing aerospace parts. Image via Mikayla Heineck, US Air Force.
The US Air Force is also using Senvol’s ML software to optimize the settings of its EOS 3D printers for producing aerospace parts. Image via Mikayla Heineck, US Air Force.

Optimizing the Army’s 3D printing process

In the latest application of its ML software, Senvol has been chosen by the ARL to design and qualify 3D printed parts at a rapid pace. Using its proprietary algorithms, the company will attempt to develop a universal qualification plan that not only requires fewer builds, but can be applied to any part, process or printer. 

Senvol’s software is already machine agnostic, which should make it well-suited to the development of a flexible process that’s compatible with as many machine models as possible. What’s more, the company’s program is also capable of simultaneously qualifying 3D printing methods and material design allowables. 

Consequently, the amount of data generation required during the ML process should be minimal, which could ultimately allow Senvol to qualify parts quickly and efficiently. During the project, which is being run by the National Center for Manufacturing Sciences, the company will also partner with Lockheed Martin, EWI, and Pilgrim Consulting.

The joint-project’s eventual goal is to fabricate a missile part and compare its performance during testing to the simulations provided by Senvol’s ML software. If successful, these evaluations would not only validate the component in question, but also Senvol’s technology for certifying other military devices as fit for purpose. 

“Despite the potential that AM offers, the rate of adoption is very slow due to the high cost and time associated with design and qualification,” said Stephanie Koch, who manages the ARL program. “We are very encouraged with Senvol’s approach, and look forward to seeing how we can leverage machine learning to improve processes.”

Rocket Lab delivers bumper payload into orbit 

California-based aerospace firm Rocket Lab has also made progress with its projectiles recently, and in the 16th launch of its additive Electron rocket, it successfully fired 30 satellites into orbit. Not only did the mission represent the company’s largest so far, but it was also the first to complete a “splashdown” return, enabling its first stage to be used again. 

However, the missile’s Rutherford engines weren’t the only 3D printed products on-board, as an additive replica of Gnome Chompski from Valve’s Half Life 2 video game was also strapped to its kick stage. Chompski is part of a fundraiser in which the firms have pledged to donate $1 (USD) for every streamer of the launch, to the Starship Children’s Hospital.

So far, $80,000 has been raised for the campaign, and Rocket Lab will continue to donate for each stream in the 24 hours following the rocket’s launch. Sadly though, Gnome Chompski remains attached to the rocket, and he will deorbit with it when the stage burns up on re-entry to the Earth’s atmosphere.

“What the team achieved today in recovering Electron’s first stage is no mean feat,” said Peter Beck, CEO and Founder of Rocket Lab. “It took a monumental effort from many teams across Rocket Lab, and it’s exciting to see that work pay off in a major step towards making Electron a reusable rocket.” 

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Featured image shows two U.S. soldiers testing a missile launcher as part of a joint research project with John Hopkins University. Photo via the ARL.



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