June 19, 2020 | International, Aerospace, Naval, Land, C4ISR, Security
NAVY
BAE Systems Technology Solutions and Services Inc., Rockville, Maryland, is awarded an $85,912,640 cost-plus-fixed-fee, cost reimbursable, indefinite-delivery/indefinite-quantity contract. This contract provides for in-service engineering activity and production services for various Navy identification and data link systems in support of the Combat Integration and Identification Systems Division at the Naval Air Warfare Center Webster Outlying Field. Work will be performed in Patuxent River, Maryland (77%); and Rockville, Maryland (23%). Services will support integration and production efforts, including design and feasibility evaluation, component and system design, system integration, production, installation testing and evaluation, in-service engineering, logistics, repair and validation, training, lab maintenance, quality assurance and technical management on a worldwide range of naval ship and shore platforms. Work is expected to be complete by June 2025. No funds will be obligated at the time of award. Funds will be obligated on individual orders as they are issued. This contract was competitively procured via an electronic request for proposal, and one offer was received. The Naval Air Warfare Center Aircraft Division, Patuxent River, Maryland, is the contracting activity (N00421-20-D-0117).
BAE Systems Land & Armaments LP, Minneapolis, Minnesota, is awarded an $18,771,034 firm-fixed-price modification to previously awarded contract N00174-17-C-0022 to exercise Option Year Three for the fiscal 2017-2020 production of the MK 38 MOD 3 machine gun system (MGS) and associated spares. Work will be performed in Hafia, Israel (67%); and Louisville, Kentucky (33%). The production of the MGS is derived from the application of an ordnance alteration to the MK 38 MOD 1 25mm MGS. Once installed, this version will incorporate two-axis stabilizations, an improved electro-optical sight system, improved multi-function display, a modified main control panel, a new main computing unit, a 7.62mm machine gun and remote control operation. Work is expected to be complete by November 2021. Fiscal 2020 weapon procurement (Coast Guard) funds; 2017 shipbuilding and conversion (Navy) funds; and 2020 weapon procurement (Navy) funds in the amount of $18,771,034 will be obligated at the time of award. Funds will not expire at the end of the current fiscal year. The Naval Surface Warfare Center, Indian Head Explosive Ordnance Disposal Technology Division, Indian Head, Maryland, is the contracting activity.
AV3 Inc., Mechanicsville, Maryland, is awarded a $9,770,558 firm-fixed-price, indefinite-delivery/indefinite-quantity contract. This contract procures the audio and visual video teleconference equipment for the integration of specialized network video teleconference systems in support of the integrated command, control and intelligence divisions of the Joint Staff and combatant commanders, Department of Defense agencies and services, and Department of Homeland Security operational and support components. Work will be performed in Mechanicsville, Maryland, and supports the command, control, communications, computers, cyber, intelligence, surveillance and reconnaissance missions. Work is expected to be complete by June 2022. No funds will be obligated at the time of award. Funds will be obligated on individual orders as they are issued. This contract was competitively procured via an electronic request for proposal and two offers were received. The Naval Air Warfare Center Aircraft Division, Lakehurst, New Jersey, is the contracting activity (N68335-20-D-0028).
ARMY
Raytheon, Fort Wayne, Indiana, was awarded a $29,237,124 hybrid (cost-plus-fixed-fee and firm-fixed-price) contract for procurement of Jordan's Advanced Field Artillery Tactical Data System. Bids were solicited via the internet with one received. Work will be performed in Fort Wayne, Indiana, with an estimated completion date of Oct. 31, 2024. Fiscal 2020 Foreign Military Sales (Jordan) funds in the amount of $29,237,124 were obligated at the time of the award. U.S. Army Contracting Command, Aberdeen Proving Ground, Maryland, is the contracting activity (W91CRB-20-C-5016).
Sigmatech Inc.,* Huntsville, Alabama, was awarded an $8,220,049 modification (000240) to contract W31P4Q-15-A-0028 for technical support for the unmanned aircraft systems project manager's office. Work will be performed in Huntsville, Alabama, with an estimated completion date of June 18, 2021. Fiscal 2020 operations and maintenance (Army) funds in the amount of $8,220,049 were obligated at the time of the award. U.S. Army Contracting Command, Redstone Arsenal, Alabama, is the contracting activity.
DRS Sustainment Systems Inc., St. Louis, Missouri, was awarded a $7,985,880 modification (P00069) to contract W56HZV-16-C-0028 for seven Joint Assault Bridge Systems. Work will be performed in West Plains, Missouri, with an estimated completion date of May 11, 2024. Fiscal 2019 other procurement (Army) funds in the amount of $7,985,880 were obligated at the time of the award. U.S. Army Contracting Command, Detroit Arsenal, Michigan, is the contracting activity.
DEFENSE LOGISTICS AGENCY
DMG Mori USA Inc., Hoffman Estates, Illinois, has been awarded a maximum $17,302,222 firm-fixed-price contract for integrated manufacturing cell axis machining centers and machines. This was a sole-source acquisition using justification 10 U.S. Code 2304 (c)(1), as stated in Federal Acquisition Regulation 6.302-1. This is a two-year contract with no option periods. Locations of performance are Illinois; California; and Germany, with a March 3, 2022, performance completion date. Using military service is Navy. Type of appropriation is fiscal 2020 through 2022 defense working capital funds. The contracting activity is the Defense Logistics Agency Aviation, Richmond, Virginia (SPE4A8-20-C-0007).
Honeywell International Inc., Phoenix, Arizona, has been awarded a maximum $7,785,286 firm-fixed-price, indefinite-delivery/indefinite-quantity delivery order SPRRA1-20-F-0197 against a one-year contract (SPRRA1-20-D-0038) with no option periods for clutch assemblies. This was a sole-source acquisition using justification 10 U.S. Code 2304 (c)(1), as stated in Federal Acquisition Regulation 6.302-1. Location of performance is Arizona, with a June 30, 2021, ordering period end date. Using military service is Army. Type of appropriation is fiscal 2020 through 2021 Army working capital funds. The contracting activity is the Defense Logistics Agency Aviation, Redstone Arsenal, Alabama.
CORRECTION: The contract announced on June 16, 2020, for Pentaq Manufacturing Corp., Sabana Grande, Puerto Rico (SPE1C1-20-D-1258), for $33,645,750 was announced with an incorrect award date. The correct award date is June 17, 2020.
CORRECTION: The delivery order (SPRRA2-20-F-0087) announced on May 27, 2020, for Raytheon Co., Andover, Massachusetts (SPRBL1-15-D-0017), for $14,494,050 was announced with an incorrect dollar amount. The correct dollar amount is $14,971,905.
U.S. TRANSPORTATION COMMAND
Science Applications International Corp., Reston, Virginia, has been awarded hybrid (labor hour and firm-fixed-price) task order HTC711-20-F-D061 in the amount of $8,863,576. The task order provides software engineering services to the U.S. Military Surface Deployment and Distribution Command. Requirement to obtain software engineering services to support U.S. Military Surface Deployment and Distribution Command's integrated booking system. Services include requirements definition, software maintenance, development, configuration management, area manager support, training, implementation, documentation, technical support and project management. Work will be performed at Scott Air Force Base, Illinois. The contract base period of performance is from Oct. 1, 2020, to Sept. 30, 2021. No funds were obligated at award; award was made subject to the availability of fiscal 2021 funds. U.S. Transportation Command, Directorate of Acquisition, Scott Air Force Base, Illinois, is the contracting activity.
*Small Business
https://www.defense.gov/Newsroom/Contracts/Contract/Article/2225212/source/GovDelivery/
September 18, 2023 | International, Aerospace
The U.S. military said on Monday it was still searching for an F-35 fighter jet after a mishap on Sunday near an air base in South Carolina and has asked for the public's help locating it.
July 5, 2021 | International, Aerospace, Naval, Land, C4ISR, Security
Chris Kubasik replaces Bill Brown and becomes the second CEO in the history of the U.S. company, which formed in mid-2019 when Harris Corporation and L3 Technologies merged into a single business.
October 16, 2018 | International, Naval
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