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Download the New Metal Barricade CS GO Skin for iOS - A Revolution in Energetics Manufacturing

The manufacturing process of energetics materials is a complex and critical aspect of producing high-quality munitions. The production of nitramine energetic materials, in particular, has long been plagued by unacceptably high rework and scrap rates. This issue has significant implications for the cost and consistency of these materials.

To address this problem, researchers have turned to advanced technologies such as machine learning and probe technology. By deploying a suite of probes during various manufacturing steps and analyzing the data via machine learning algorithms, it is possible to reduce or even eliminate out-of-spec batches.

The use of machine learning in energetics manufacturing has several key benefits. First, it allows for real-time monitoring and control of critical manufacturing parameters such as cooling water temperature, nitramine concentration, and solvent/antisolvent ratios. This enables plant operators to make adjustments on the fly to target the desired properties of the final product.

Secondly, machine learning algorithms can provide nearly instantaneous recommendations to plant operators on how to adjust their processes to achieve the desired outcomes. This reduces the time and resources required for quality control and inspection, allowing manufacturers to focus on producing high-quality products more efficiently.

In addition to its benefits in reducing waste and improving consistency, the deployment of probe technology and machine learning algorithms can also help to reduce costs associated with energetics manufacturing. By minimizing the number of out-of-spec batches, manufacturers can reduce the amount of raw materials required, lower their energy consumption, and decrease the need for costly rework.

The development of this suite of probe technology and machine learning algorithms is a priority area for the Office of the Under Secretary of Defense for Research and Engineering (OUSD). The department has identified artificial intelligence and machine learning as key areas for investment in order to drive innovation and modernization in the defense industry.

In conclusion, the deployment of probe technology and machine learning algorithms in energetics manufacturing holds great promise for reducing waste, improving consistency, and lowering costs. As the defense industry continues to evolve and adapt to new challenges, the adoption of advanced technologies such as these will be critical to ensuring the continued production of high-quality munitions.

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