Courtesy Photo | A primary responsibility of U.S. Army Yuma Proving Ground (YPG) is to support...... read more read more
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The U.S. Army wants to reduce sensor to shooter timelines, react to threats faster, and combine all of the systems and effects available at their disposal to be ready to defend against near-peer symmetric warfare.
Army senior leaders say that successful deterrence against near-peer adversaries with the ability to conduct large-scale combat operations will require multidomain operations and leveraging new technologies across all military branches.
It is a tall and complex order, and U.S. Army Yuma Proving Ground (YPG) is at the forefront of conducting developmental testing of the equipment likely to be relied on by the future force.
Data has always been YPG’s chief product for the Army, and in conjunction with the Army Test and Evaluation Command (ATEC) the post is developing the local architecture and data governance procedures in advance of more practical case uses for artificial intelligence (AI) in support of the test mission. The working groups mapping out the future of both test data and enterprise data are likely to have impact across ATEC.
“Yuma’s been in a position where we have a pretty broad mission area because we are testing in extreme natural environments,” said Ross Gwynn, YPG Technical Director. “All the processes that take an extensive amount of time on the front end to get a test set up or to get data cleaned after collection are being optimized. By doing so, we are thinking about the system under test and analyzing the data a little more as opposed to repetitive tasks taking the majority of an analyst’s time.”
From vision-based AI learning to automating Kineto Tracking Mount-calibration processes, the proving ground is already reaping dividends from the most recent technology, and more benefits are expected as the impact of the efficiency gains continues.
“Now we have a little more time on our hands to think about how we could advance another part of some other process we use,” said Gwynn. “We’re starting to think in a more valuable manner and applying ourselves to finding ways to advance capabilities as opposed to just utilizing the current capability to keep the wheels moving around here. We’re doing things better, smarter, and faster by applying recent technology to the right spaces.”
One benefit that YPG has over other organizations is troves of historical data from decades of test events. This data is extremely valuable for training AI models to automate or expedite data reduction and analysis. A recent successful example involved developing a workable algorithm to help facilitate the acoustic trilateration of air to surface missiles and other helicopter rounds collected from arrays of microphones and hydrophones on the post’s highly instrumented ranges. Additionally, the post’s Air Delivery Branch is building a new test data structure with the collaborative efforts of longtime YPG analysts and a developer.
“It is very discreet, isolated data sources that we understand and subject matter experts that know that instrumentation really well to partner with the contractor and get to a data structure where we’re automating the front end of this process,” said Gwynn. “If we really streamline the process from data collection, reduction, and analysis, we can get much more efficient and have additional time to apply more analytical thought to the testing we just did.”
Business data is also an important component of data governance, particularly in things like tracking equipment lifecycles and utilization rates—for example, is a particular piece of equipment spending an excessive amount of time in maintenance?
“It’s a huge advantage we would have as managers to use those resources,” said Gwynn. “If we can automate that stream of business data in a similar manner as test data, it would have a lot of upside in efficiency gains.”
Another prime candidate to benefit is YPG’s non-destructive testing capability, which has long been considered the premier facility within the Army. With decades of data from laser-bore measuring of artillery tubes as they wear, an AI could be trained to help assess possible problems and predict expected life of the tubes. Potentially, the capacity to precisely monitor gun tube wear could eventually be in the hands of soldiers operating artillery systems in theater.
“Using in-bore pictures, laser scans, and other physical measurements from various inspection technologies, an AI could analyze and correlate past and current failures across all these data sources, compiling them into a comprehensive report for our test customers,” said Savanna Silva, YPG Metrology Branch Chief. “We’re not stopping there: we aim to take it further by developing AI capable of performing predictive wear analysis on weapon systems. This would integrate data from both fielded and experimental ammunition test at YPG. The goal is to perfect this process and system here at YPG, and eventually provide an all-in-one system solution that can be provided to soldiers at the depot level for use in their weapon inspections.”
The metrology branch is also seeking to train an AI that can monitor the life cycle of piezoelectric pressure transducers used in testing artillery here.
"We've never fully characterized how tourmaline crystals in our piezoelectric pressure transducers behave over time or under repeated high-pressure events, as we've always assumed how they would perform," said Silva. "Our high-pressure tests have always been single use only for the transducers. Now, it's time to refine our approach. By leveraging our data and using AI to analyze it, we can gain a much deeper understanding of their performance."
The efforts are also reaping rewards in less measurable ways.
“It is a morale builder,” said Gwynn. “People get excited when you are doing something different and making a difference.”
| Date Taken: | 01.08.2025 |
| Date Posted: | 01.08.2025 13:32 |
| Story ID: | 487337 |
| Location: | YUMA PROVING GROUND, ARIZONA, US |
| Web Views: | 282 |
| Downloads: | 0 |
This work, Yuma Proving Ground looks to artificial intelligence for data efficiency gains, by Mark Schauer, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
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