MxD partners with our top-tier members (solution providers and manufacturers), academic researchers, startups and the U.S. government to launch digital manufacturing projects with commercial potential. Collaborative projects like these help our partners solve digital problems outside of their core competencies.
MxD has partnered with Siemens and Fast Radius to demonstrate how 3D printing, rapid prototyping, and other digital technology can improve response times in the manufacture of point-of-care of medical devices.
The University of Tennessee, Knoxville, and Third Wave Systems will optimize computer numerically controlled (CNC) machining parameters using a combination of physics-based models, in-process data, and machine learning to reduce scrapped parts.
The University of North Carolina at Charlotte and Siemens will develop a digital twin tool that enables manufacturers to run cybersecurity tests on virtual replicas of the physical machines and networks on their factory floor.
This phase of the project continues development of the Docent Manufacturing Readiness Assessment software tool with the key objective of establishing a commercial-ready solution with a baseline sustainability model for the support and growth of the tool.
This project will knit together best-in-class technology to produce a modularized machine learning-enabled software component (Dynamic Risk Mitigation Engine) that integrates supplier, bill-of-material, and event-based risk information to produce recommendations aimed at preventing supply chain disruptions before they happen.
This project will develop actionable resources to help small and medium manufacturers (SMMs) “get started” in digital manufacturing through implementing detailed case studies at SMM testbeds.
Phase II of the Pathfinder project covers the implementation of the security measures that have been identified during Phase I for the evaluation a Markforged 3D printer. During Phase II, the project team will continue to expand and increase security in the manufacturing and use of 3D printers in order to provide assurance for the machines to connect to enterprise networks.
This project will develop a new framework for a human-centered digital twin by developing and demonstrating a viable means to measure worker motion and fatigue during manufacturing assembly tasks.
Rutgers is partnering with Texas A&M Engineering Experiment Station (TEES) to develop new methods to secure voice control technology and help manufacturers defend themselves against voice-based cyber attacks.
Penn State will partner with Siemens to develop a technique to help manufacturers reduce the risk of privacy breaches during model inversion cyberattacks. In such attacks, an adversary uses model output to steal sensitive raw data.
Boeing is leading this project, which will use metrology to develop standards for enhanced precision of industrial robots. Team members from Boeing, API, Georgia Institute of Technology, Hexagon and Missouri University of Science and Technology will then evaluate the standards on two testbeds.
The Rochester Institute of Technology is partnering with Dow on a project to optimize how multi-echelon inventory policies are designed and implemented.
Penn State Applied Research Lab leads this project that aims to create a way to use deep learning, a subset of machine learning, to perform automated analyses of product lifecycle management (PLM) system components and detect data anomalies.
MxD is partnering with Illumina, Horizon Controls Group, the International Academy of Automation Engineering, and MIT to demonstrate how Digital Twin technology can improve the medical product industry’s resiliency during emergencies.
This project will demonstrate the benefits of real-time closed-loop production scheduling for large-scale as well as small- and medium-sized manufacturers.
The goal of this project is development and deployment of an online digital twin framework that will securely, reliably, and efficiently deliver the best part quality possible in additive manufacturing.
This project will identify the cybersecurity problems that small- and medium-sized manufacturers (SMMs) face with the increase in networked operational technology (OT) equipment on the factory floor. It will then recommend solutions and build a case for investment in cybersecurity infrastructure.
This project will investigate the various wireless technologies that can be used for secure factory operation in industrial environments and will provide guidance via an interactive selection tool.
This project will create an interactive test bed on MxD’s Factory Floor that will demonstrate the various wireless technologies available for secure factory operation in industrial environments.
This project will create an online system to link consumers, manufacturers, and designers to meet urgent needs during healthcare emergencies that disrupt supply chains.
This project will create a software-based data-exchange platform to enable the sharing of technical information throughout a product’s lifecycle to securely increase collaboration among suppliers.
The Collaboration Project Team will develop a secure communication framework enabling virtual verification and validation of parts across additive manufacturing (AM) types and platforms. This includes universal data, types of sensors available per machine, sampling rate, and interdependence data.
Northern Illinois University and Triangle Package Machinery Co. will develop a machine learning-based quality improvement tool to help operators and inspectors maintain consistent high-quality parts production when using the thermal energy cutting process.
The goal of this project is to develop a playbook for designing and implementing artificial intelligence-based predictive maintenance solutions, focusing on multistage roll-to-roll (R2R) manufacturing while also documenting work done in discrete manufacturing.
MxD is implementing at-machine part inspection and a part-quality dashboard at Rock Island Arsenal-Joint Manufacturing and Technology Center (RIA-JMTC).
The purpose of this project is to document the state of the Rock Island Arsenal-Joint Manufacturing and Technology Center’s (RIA-JMTC) network and add wireless connectivity.
This Rock Island Arsenal-Joint Manufacturing and Technology Center (RIA-JMTC) project aims to enhance visibility of networked equipment in a high-mix, low volume, specialized manufacturing setting, allowing for better monitoring of current projects and optimizing readiness for future projects.
This project will deliver a report on installation of an automated paint/coating path at the Rock Island Arsenal-Joint Manufacturing and Technology Center (RIA-JMTC), which is exploring implementation of a robotic paint system.
The objective of this Rock Island Arsenal-Joint Manufacturing and Technology Center (RIA-JMTC) project is to quantify the effort needed to transition RIA-JMTC from Siemens NX software to PTC Creo software.
This MxD project will create a web portal to give the small and medium-sized manufacturing (SMM) community a clear view of operational technology (OT) cybersecurity standards and industry guidelines and provide simplified guidance.
This project aims to develop, test, and demonstrate an Internet of Things (IoT) architectural framework for integrating endpoint devices as sensor technologies that assess common worker safety risks in a manufacturing environment.
This project applies Bayesian machine learning to the selection of optimized milling parameters.
This project aims to develop a Digital Manufacturing Playbook, a practical, easy-to-use video guide to assist small and medium-sized manufacturers (SMMs) in their transition to digital manufacturing.
This project will create a web-based tool that will generate tailored cybersecurity playbooks to help organizations implement industrial control system (ICS) responses to a variety of attack vectors.
Identifying opportunities to develop and implement Advanced Manufacturing Technologies in critical processes of existing products.
The goal of this project is to identify opportunities to develop and implement advanced manufacturing technologies to streamline — and gather data about — tooling used in research and development (R&D).
This project aims to explore the uses and applications of blockchain technology for supply chain management for manufacturers.
The goal of this project is development of a platform using artificial intelligence (AI) video analytics technology to give an accurate ergonomics risk assessment by providing real-time performance data to manufacturers and workers.
MxD has partnered with Siemens to develop an active splitter system to support usage of a single ventilator by two patients with different lung capacities and to enhance the demonstration cases of the “Lungs-in-the-Loop” testbed deployed on the MxD Factory Floor.
The project aims to design and develop new privacy-preserving algorithms for distributed artificial intelligence (AI) systems, with a focus on mitigating privacy breaches and decision-system disturbances and maximizing capabilities of AI models in autonomous manufacturing.
This first-of-its-kind, highly flexible testbed will showcase the advantages of open process automation (OPA) and demonstrate the value that a digital twin and machine learning (ML) technologies can provide for manufacturers.
MxD has partnered with Siemens, MK Morse, and Kent Displays to advance in situ quality inspection in manufacturing processes by developing the Smart Defect Analysis and Remediation for Product Quality Assurance (Smart-DARPQuA).
PROBLEM
Discrete manufacturers waste material when die-casting complex metal parts.
PROBLEM:
More than half of all small-to-medium-size businesses have experienced a cyberattack in the last 12 months.
The National Center for Manufacturing Sciences leads a team to develop a sensor retrofit kit. The kit communicates data through an industry standard MTConnect interface to a cloud dashboard.
MxD sponsors a study to assess how small and medium-sized manufacturers are complying with DoD’s new cybersecurity rules.
MxD and ManpowerGroup release a digital workforce taxonomy, a groundbreaking analysis that identifies 165 new data-centric manufacturing jobs—roles like “collaborative robotics technician” and “predictive maintenance systems specialist.”
Dow, Microsoft, ITAMCO, and two universities team up to build a supply chain risk alert system that will warn manufacturers further down the supply chain of delays caused by emergencies, weather, or natural disasters.
The process demonstration skid (PDS) built at the MxD Future Factory is accompanied by a full software stack designed to demonstrate the use of modern mobile technologies.
The project outcomes contained reporting documents such as, the System Security Plan which documents detailed system categorization and system boundaries along with a risk assessment report which is a workbook identifying current system vulnerabilities and security objectives and finally, a STIG checklist which provides a grading sale for each security technical implement guide (rule).
A middleware platform (application) along with educational documentation such as reports, recorded demonstrations, and a best practice supply chain playbook.
The team developed a manufacturing/production system model to be integrated within a digital enterprise (data, models, etc.) that will provide decision-makers with a methodology aimed at capturing manufacturing and production system considerations in a model-based environment.
The objective of the project is to develop a close-loop AI inspection system integrated with Digital Thread to provide timely, actionable insights about the quality of the current adhesive dots.
The team demonstrated the technological feasibility and validity of a threat intelligence-driven cyber threat mission builder.
Techsolve, Inc. will partner with Siemens and several other MxD partners to benchmark and evaluate the OT and IT cybersecurity tools currently available to small and medium-size manufacturers.
The goal of this project was to develop an open architecture digital twin testbed enabling “plug-and-play” interoperability with technologies from various vendors for sensing, data aggregation, analytics, and control.
This project will develop and implement supply chain visibility tasks within the Critical Infrastructure Resilience Institute (CIRI) Cybersecure Dashboard. CIRI is a Department of Homeland Security Center of Excellence research organization with a mission to positively impact the security and resilience of critical infrastructure around the nation.
The goal of this project was to objectively determine an optimal configuration of 5G for industrial buildings — specifically for industrial internet of things (IIoT) and digital applications — and to make this information publicly available for broad use including by the Department of Defense (DoD).
This project delivered an unbiased guidebook that 1) directs manufacturers on how to implement wireless network architecture in industrial facilities and 2) provides instruction on how to transition existing wired architecture to a 5G wireless network.
This project created and implemented a private 5G system on MxD’s factory floor that served as the base for a testbed and demonstrated and assessed 5G wireless network configurations for industrial applications.
This project’s goal was to survey, document, and provide guidance on implementation of state-of-the-art options for cybersecurity protection in a private 5G network system.
Leveraging artificial intelligence and machine learning (AI/ML) cloud models developed with manufacturing data, CODA helps design engineers reduce engineering change orders (ECOs).
The LHAS aimed to capture users’ health indicators, translate those indicators to create a risk assessment, and use that risk assessment data to inform the public health supply chain about possible demand surges.
This project investigated disparities between designed multi-echelon inventory policies and their implementation and developed ways to mitigate them by improving policy design input or providing tools for better execution.
The Collaboration Project Team will develop a secure communication framework enabling virtual verification and validation of parts across additive manufacturing (AM) types and platforms. This includes universal data, types of sensors available per machine, sampling rate, and interdependence data.
The project team delivered a prototype Technical Data as a Service (TDaaS) solution that streamlines and enhances the management and utilization of technical data packages (TDPs) for a large manufacturer and its extensive ecosystem of suppliers and partners.
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Facts & Figures
Facts & Figures
More than 60 external projects across more than 35 states.
Facts & Figures
Facts & Figures
$90 million
Facts & Figures
Facts & Figures
Through operational efficiencies, predictive and preventative maintenance, better supply chain management, and inventory optimization.
Facts & Figures
Facts & Figures
They boil down to the same concept: it’s all about applying information and communication technology to manufacturing.
Tap the square to reveal the answer
Facts & Figures
Facts & Figures
More than 60 external projects across more than 35 states.
Facts & Figures
Facts & Figures
$90 million
Facts & Figures
Facts & Figures
Through operational efficiencies, predictive and preventative maintenance, better supply chain management, and inventory optimization.
Facts & Figures
Facts & Figures
They boil down to the same concept: it’s all about applying information and communication technology to manufacturing.