U.S. Army considers German-built armored combat vehicle, with U.S. sensors and embedded computing

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  12 mai 2024 | International, Aérospatial

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  16 octobre 2018 | International, Naval

  How the Office of Naval Research hopes to revolutionize manufacturing

  By: Daniel Cebul WASHINGTON — The Office of Naval Research awarded Lockheed Martin Oct. 1 a two-year, $5.8 million contract to explore how machine learning and artificial intelligence can make complex 3-D printing more reliable and save hours of tedious post-production inspections. In today's factories, 3-D printing parts requires persistent monitoring by specialists to ensure intricate parts are produced without impurities and imperfections that can compromise the integrity of the part overall. To improve this laborious process, the Navy is tasking Lockheed Martin with developing multi-axis robots that use lasers to deposit material and oversee the printing of parts. Lockheed Martin has multiple partners on the contract including Carnegie Mellon University, Iowa State University, Colorado School of Mines, America Makes, GKN and Wolf Robotics and Oak Ridge National Laboratory. The contract covers what Glynn Adams, a senior engineer with Lockheed Martin, describes as the pre-flight model of the program's development. Initial work will focus on developing computer models that can predict the microstructures and mechanical properties of 3-D printed materials to generate simulation data to train with. Adams said the Carnegie Mellon team will look at variables such as, “the spot size of the laser beam, the rate of feed of the titanium wire [and]the total amount energy density input into the material while it is being manufactured.” This information helps the team predict the microstructure, or organizational structure of a material on a very small scale, that influences the physical properties of the additive manufactured part. This data will then be shared with Iowa State, who will plug the information into a model that predicts the mechanical properties of the printed component. By taking temperature and spot size measurements, the team can also ensure they are, “accurately controlling energy density, the power of both the laser and the hot wire that goes into the process,” Adams said.. “All of that is happening before you actually try to do any kind of machine learning or artificial neural networks with the robot itself. That's just to try to train the models to the point where we have confidence in the models,” Adams said. Sounds easy, right? But one key problem could come in cleaning up the data and removing excess noise from the measurements. “Thermal measurements are pretty easy and not data intensive, but when you start looking at optical measurements you can collect just an enormous amount of data that is difficult to manage,” Adams explained. Lockheed Martin wants to learn how shrink the size of that dataset without sacrificing key parameters. The Colorado School of Mines and America Makes will tackle the problem of compressing and manipulating this data to extract the key information needed to train the algorithms. After this work has been completed, the algorithms then will be sent to Oak Ridge National Laboratory, where robots will begin producing 3-D titanium parts and learn how to reliably construct geometrically and structurally sound parts. This portion of the program will confront challenges from the additive manufacturing and AI components of the project. On the additive manufacturing side, the team will work with new manufacturing process, “trying to understand exactly what the primary, secondary and tertiary interactions are between all those different process parameters,” Adams said. “If you think about it, as you are building the part depending on the geometric complexity, now those interactions change based on the path the robot has to take to manufacture that part. One of the biggest challenges is going to be to understand exactly which of those parameters are the primary, which are the tertiary and to what level of control we need to be able to manipulate or control those process parameters in order to generate the confidence in the parts that we want.” At the same time, researchers also will tackle AI machine learning challenges. Like with other AI programs, it's crucial the algorithm is learning the right information, the right way. The models will give the algorithms a good starting point, but Adams said this will be an iterative process that depends on the algorithm's ability to self-correct. “At some point, there are some inaccuracies that could come into that model,” Adams explained. “So now, the system itself has to understand it may be getting into a regime that is not going to produce the mechanical properties or microstructures that you want, and be able to self-correct to make certain that instead of going into that regime it goes into a regime that produces the geometric part that you want.” With a complete algorithm that can be trusted to produce structurally sound 3-D printed parts, time-consuming post-production inspections will become a thing of the past. Instead of nondestructive inspections and evaluations, if you “have enough control on the process, enough in situ measurements, enough models to show that that process and the robot performed exactly as you thought it would, and produced a part that you know what its capabilities are going to be, you can immediately deploy that part,” said Adams. “That's the end game, that's what we're trying to get to, is to build the quality into the part instead of inspecting it in afterwards." Confidence in 3-D printed parts could have dramatic consequences for soldiers are across the services. As opposed to waiting for replacement parts, service members could readily search a database of components, find the part they need and have a replacement they can trust in hours rather than days or weeks. “When you can trust a robotic system to make a quality part, that opens the door to who can build usable parts and where you build them,” said Zach Loftus, Lockheed Martin Fellow for additive manufacturing. “Think about sustainment and how a maintainer can print a replacement part at sea, or a mechanic print a replacement part for a truck deep in the desert. This takes 3-D printing to the next, big step of deployment.” https://www.c4isrnet.com/industry/2018/10/15/how-the-office-of-naval-research-hopes-to-revolutionize-manufacturing

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  24 juillet 2020 | International, C4ISR, Sécurité

  Thales launches a new integrated 24/7 NOC-SOC in the Netherlands

  July 21, 2020 - Thales has expanded managed services for its customers with the launch of a new integrated Network Operations Center (NOC)/ Cybersecurity Operations Center (SOC) in the Netherlands. With trained experts present 24/7, the integrated NOC-SOC can provide organisations with premium services for IS-IT asset management and cybersecurity supervision, a critical necessity following the explosion of remote working during the Covid-19 crisis. With more than 40 years of expertise, Thales already serves more than 40 clients around the world through its five existing Cybersecurity Operations Centres (Canada, France, Hong Kong, Netherlands, United-Kingdom). Forming part of Thales's international network of premium Cybersecurity Operations Centres, the Group's first integrated Network Operations Centre (NOC) and Security Operations Centre (SOC) will simultaneously monitor customers' IT and OT infrastructure 24/7. Since IT/OT assets in the new NOC/SOC are monitored from the Netherlands, data remain in the country and sensitive information is viewed only by screened personnel. Being able to deliver these secure integrated managed services in the Netherlands is a first for Thales. As a rule, organisations outsource night-time monitoring to SOCs in other countries. From now on, Thales will be able to offer this service for and from the Netherlands. The NOC currently analyses anonymised transaction data from public transport companies 24/7 in order to rectify faults, and the SOC focuses on monitoring the computer and network activities of critical infrastructure companies, while keeping the networks physically separate. The NOC monitors mainly systems availability, while the SOC monitors cyber security. This enables both services to intervene quickly in the event of an incident, shortening any downtime and reducing damage. Now the two centres have been merged so that the teams have everything at their disposal to further optimise service levels and meet the highest standards of security. SOC and NOC employees are screened and trained to meet far-reaching Dutch quality standards and SOC services fully comply with Dutch legislation and regulations (ISO 27001 and NEN 7510). Thales has more than 15 years' experience in managed cybersecurity services worldwide. The Group is positioned as the trusted partner of choice for the most demanding organisations worldwide in terms of cybersecurity, operating five premium Cybersecurity Operations Centers around the world, in France, the United Kingdom, the Netherlands, Canada and Hong Kong. "I am proud of this next step in our provision of services, as a result of which we are the first to offer 24/7 monitoring of assets and IT on Dutch soil," said Mark Donderwinkel, VP Thales Secure Communications and Information Systems."As a result of Covid-19, much more use is being made of remote collaboration tools. This is making organisations more vulnerable, and the number of cyber attacks is rising sharply. Now that we are in a phase of opening up our infrastructure and networks, it is crucial that downtime is kept to the absolute minimum. In order to achieve this, better monitoring is necessary, both of assets and of computer and network activities. We can now provide our customers with the highest and continuous level of service for both asset management and cyber security." View source version on Thales: https://www.thalesgroup.com/en/group/press-release/thales-launches-new-integrated-247-noc-soc-netherlands-0