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You are here: Home / Manufacturing / Advanced Manufacturing / SecureAmerica Institute announces $5M in funding to advance U.S. manufacturing resiliency

SecureAmerica Institute announces $5M in funding to advance U.S. manufacturing resiliency

November 18, 2020 By SecureAmerica Institute

An engineer is programming robotic arm with control panel (teach pendant) which is working on smart factory production line.
The SecureAmerica Institute released an inaugural project call in August as part of a nationwide initiative to empower a secure and resilient U.S. manufacturing and defense industrial base. | Getty Images

By: Lauren Thompson, SecureAmerica Marketing & Communications

The SecureAmerica Institute (SAI), led by the Texas A&M Engineering Experiment Station, a member of The Texas A&M University System, released an inaugural project call in August as part of a nationwide initiative to empower a secure and resilient U.S. manufacturing and defense industrial base.

Technical projects from Deloitte, Morgan State University, Ohio State University, New York University, Raytheon Technology Research Center, University of Texas Dallas, Amentum, North Carolina State University, and URSA Major Technologies were competitively selected for a total funding portfolio of more than $5 million.

“The selected projects are grounded in SecureAmerica’s technical focus areas and domains,” said Rob Gorham, SAI executive director. “They will build on our core capabilities to expand the U.S. manufacturing and defense industrial base and maintain leadership in the development and deployment of innovative manufacturing technologies and products.”

Selected projects are:

“Deep View”
Organization: Deloitte

Vital industrial bases no longer have any domestic presence creating a gap in an American ability to remain resilient during potential disruptions. Deep View will conduct a multi-tier supply chain and industrial base assessment to evaluate the technology readiness levels of interconnection security within the selected manufacturing industrial base. Project findings will be incorporated into SAI’s long-term engagement plan with industry members and raise industrial base and supply chain analysis where collective influence of SAI members can inform industry-wide risk mitigation efforts.

“Readiness Assessment of Supplier Digitization”
Organization: Morgan State University

Emerging needs and vulnerabilities in the manufacturing and defense industrial base supply chain cannot be identified without evaluating supply chain participants’ current digital maturity level across the six digitization pillars of digital development. This project will survey partners to determine the readiness of supply chain participants for satisfying the six pillars (digital development, synchronized planning, intelligent supply, smart factory, dynamic fulfillment, connected customer) to analyze and prepare a digital supply chain framework for participants.

“Methodology for Predicting and Validating the Trustworthiness of Robots”
Organization: Ohio State University

Increase in automation, specifically industrial robots, has created new access points for cybersecurity vulnerabilities in industrial internet of things (IIoT) components. This project will develop an external system consisting of sensing and human operator observations to predict when loss of trust will occur in robotic systems and identify behavior that may indicate a cyber intrusion.

“A Taxonomy-Driven Approach to Realize Built-In Security of Manufacturing Supply Chains”
Organization: New York University

 Cybersecurity by system design considerations are needed to enhance the testing and assure the built-in cybersecurity in emerging supply management chains. This project will build a global, scalable, crowdsourcing tool to help certify cybersecurity postures for all aspects of supply management systems.

“Resilient Additive Manufacturing Platform (RAMP)”
Organization: Raytheon Technologies Research Center

Additive manufacturing platforms are particularly vulnerable to cyber-attack since occurred attacks easily be mistaken as process or part defects due to inherent process variability. This project will focus on the development of machine learning based algorithms and tools to enable cyber-physical manufacturing systems to differentiate between faulty and compromised products.

“From Device to Cloud: An End-to-End Framework for Trusted Edge Computing for Industrial Internet of Things (IIoT) Security”
Organization: University of Texas at Dallas

Due to growing adoption of IIoT devices and edge computing, currently employed security solutions are insufficient to thwart strong adversaries from stealing and manipulating sensitive data. This project will develop a new trusted execution environment to enable end-to-end data protection and prevent theft of sensitive data.

“A Sensor Fusion Modeling and Simulation Framework for Decision Support”
Organization: Amentum

Few tools allow for the intuitive creation of sensor models which can then be tested across any number of scenario configurations. This project will explore an approach to leverage existing advanced sensor fusion modeling with an easy to use graphical user interface helping designers, operators, and practitioners to analyze and predict sensor-fusion based scenarios.

“Achieving Resilience Against Insider Threats in IT/OT Networks via Defensive Deception”
Organization: North Carolina State University

Improvements in manufacturing agility and flexibility through automation are needed to ensure security and resilience in information technology and operational technology networks. This project will develop solutions to achieve resilience by resisting attacks, detecting attacks in progress, and ensuring an IT/OT system can automatically restore itself to a trusted state and continue operation.

“Sensitivity Analysis of AM Process Improvements for Hypersonic Propulsion Systems”
Organization: URSA Major Technologies

Direct Laser Metal Sintering (DMLS) is a powerful manufacturing approach to creating high-performing propulsion systems for hypersonics, but is limited by the speed a component may be produced. This project will create a sensitivity analysis of a set of approaches to increase the security of additive manufacturing supply chains by improving the yield and throughput speed.

Filed Under: Advanced Manufacturing, Applied Research, Projects Tagged With: Project Call

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