MxD Projects

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.

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.

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.

The LHAS aims 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.

Leveraging artificial intelligence and machine learning (AI/ML) cloud models developed with manufacturing data, CODA will help design engineers reduce engineering change orders (ECOs).

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.

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.

Technical Data as a Service (TDaaS) intends to address problems with the interoperability and visualization of a Technical Data Package (TDP) for a large manufacturer and its extensive ecosystem of suppliers and partners.

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.