China3D printingNet November 23, the US Army Research Laboratory (ARL) has signed a contract with Senvol, the contract will use its machine learning (ML) software to help design and appraisal3D printingMissile parts.
As part of this plan, Senvol will use its ML algorithm to develop a flexible “qualification plan” that can be applied to any3D printingMethod, component or machine. Senvol President Annie Wang said that deploying the company’s ML technology will enable the US Army to reduce the delivery time and cost of parts, while improving the survivability of soldiers.
Senvol will implement data-driven machine learning technology for the US Army, which will greatly reduce the cost of material and part identification. The significant increase in speed will allow the U.S. Army to support the combat readiness of combat personnel by unlocking the full potential for change provided by additive manufacturing. “
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The method of missile parts” alt=”ARL’s purpose is to use Senvol’s ML algorithm to develop an identification
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The method of missile parts” width=”620″ height=”413″ />
The purpose of ARL is to use Senvol’s ML algorithm to develop an identification3D printingThe method of missile parts. Photo via ARL.
Military applications of machine learning algorithms
Senvol is a software developer headquartered in New York, committed to providing enterprises with enough data to3D printingIncorporate into its design and production process. The company initially established an AM database to achieve this goal in 2015, and has since expanded its product range to include a set of API, index, SOP and ML digital products.
Using its proprietary ML algorithm, Senvol can quickly establish the relationship between printer parameters and the performance of any part. The program works by calculating the best properties for a given material for users, allowing them to reduce any time spent on generating design allowances.
Although Senvol’s software has been deployed in industries ranging from automotive to medical, it is particularly effective in military applications. For example, the Office of Naval Research (ONR) of the U.S. Navy uses the ML program to reduce delivery times associated with marine components.
Recently, the U.S. Air Force has also cooperated with Senvol to provide its PBF EOS 3D printingMachine development baseline mechanical performance. After obtaining the contract awarded by ARL, the company will now try to determine a more versatile identification method and finally design an enhanced precast missile component.
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Machine settings to produce aerospace parts” alt=”The U.S. Air Force also uses Senvol’s ML software to optimize its EOS
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Machine settings to produce aerospace parts” width=”620″ height=”413″ />
The U.S. Air Force also uses Senvol’s ML software to optimize its EOS 3D printingSet up to produce aerospace parts. The picture comes from Mikayla Heineck of the US Air Force.
Optimize the Army’s3D printingProcess
Among the latest applications of its ML software, ARL chose Senvol to quickly design and appraise3D printingComponents. The company will use its proprietary algorithm to try to develop a universal certification program that not only requires less construction, but can also be applied to any part, process or printer.
Senvol’s software is already machine-independent, which should make it ideal for developing flexible processes that are compatible with as many machine models as possible.In addition, the company’s plan can also be verified at the same time3D printingMethod and material design allowable range.
China3D printingNet Comments: The amount of data generated in the machine learning process should be as small as possible, which ultimately enables Senvol to identify parts quickly and effectively. In projects managed by the National Manufacturing Science Center, the company will also collaborate with Lockheed Martin, EWI and Hajj Consulting.
The ultimate goal of the joint project is to manufacture missile components and compare their performance during testing with the simulation provided by Senvol ML software. If the evaluations are successful, these evaluations will not only verify the problematic components, but also Senvol’s technology, which can prove that other military equipment is fit for purpose.
Despite the potential of additive manufacturing technology, the adoption rate is very slow due to the high cost and time associated with design and qualification. We are very encouraged by Senvol’s approach and look forward to seeing how we can use machine learning to improve processes.
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