CESMII Offers Funding for a Wide Range of Projects
Projects fall into a variety of categories in order to deliver value to move Smart Manufacturing forward for all to take advantage of, the Democratization of Smart Manufacturing. We need to address the delivery of new technology and standards, the delivery of education, projects for proof of concepts, the delivery of local examples of success, and local demo centers. With all this in mind, CESMII is funding:
- Enabling R&D Projects – Projects to close specific technology gaps. Explore emerging technologies
- Ecosystem & Workforce Development (EWD) Projects – Develop educational content or delivery infrastructure. Build tools for framing value and driving SM adoption
- SM Platform Capability Projects – Develop core capabilities and tools
- SM Innovation Projects – Solve specific manufacturing problems. Develop Information Models & SM Apps
- SM Application Projects – Demonstrate capabilities of the Innovation Platform through real-world use cases
- SM Innovation Centers – Local examples of Smart Manufacturing benefits, tied to to vertical market applications or horizontal technologies
- Open Platform Investment Projects – Targeted projects to enable or accelerate adoption of open technologies (search for: ‘CESMII’)
Enabling R&D Projects
Enabling R&D Projects are very special. This is where CESMII funds research to solve some of the greatest challenges facing industry. Examples include the reduction of CO2 in the manufacture of Cement, or the modeling of energy use through the monitoring of equipment states, or the savings in energy by better understanding the nature of Air Separation – through the use of Digital Twins.
The results of our funded research are then shared as value for CESMII Members. Some research results may be licensed for use on a broader level. You should contact CESMII if you are interested in licensing.
The goal of this project is to develop a number of SM Platform™ ready tools and deploy them on Praxair’s commercial Air Separation Unit for improving energy efficiency.
Self-powered wireless sensor nodes and deployment of energy harvesting approaches; Efficient computational framework to acquire, post-process, and synthesize large quantities of sensory data in real time; In-process monitoring capability with offline big-data analysis techniques.
Predictive process models, data analytics, sensors and machine learning, will be developed to provide a more energy-efficient clinker production process with better quality control.
Improve steel slab quality and continuous caster productivity using Smart Manufacturing (SM) methodologies and technologies to address the top two KPIs of the continuous casting process –yield (minimize defects/rejects) and uptime (reduce UPTA).
The objective of this effort is to mitigate energy waste in manufacturing facilities, and specifically subtractive precision manufacturing, using model-based systems engineering principles.
The primary objective of this project will provide technical staff with technology to sniff out unknown or unexpected energy consumption across the facility. Prove the hypothesis that energy waste can be detected without the use of expensive energy meters.
The goal is to develop design tools and open source reference architectures that will enable engineers to reliably assess and implement data flow requirements for affordable, scalable, accessible, and portable smart manufacturing systems.
The goal of this project is to develop technologies on data modeling, machine learning and data-centric analytics for smart Aerospace additive manufacturing and to implement these innovations using data from working Aerospace manufacturing facilities.
Additional Enabling R&D Projects
- Auburn University and Rayonier Advanced Materials will develop a soft sensor and predictive control for anti-foaming agent usage, wash water flow, and pulp quality in paper manufacturing using statistics pattern analysis and machine learning. Read More
- Honeywell, Virginia Tech, Bodycote, and Seco-Warwick will develop new sensors, monitoring and data analytic methods and apply them to three industrially relevant thermal processes. Read More
- Pennsylvania State University, Texas A&M University (TAMU), and University of Texas Rio Grande Valley (UTRGV) will develop novel self-powered sensors and identify actuators needed to collect information and respond to actions for machines including legacy machines for key applications. Read More
- Rensselaer Polytechnic Institute will develop a modeling engine with sophisticated predictive capabilities to model a variety of manufacturing processes and demonstrate the capabilities on a critical complementary metal–oxide–semiconductor transistor manufacturing process. Read More
- Rutgers, The State University of New Jersey, and Janssen Pharmaceuticals will develop advanced process models, sensors and data integration architecture that will be demonstrated on wet granulation, drying and milling in pharmaceutical manufacturing processes. Read More
- Raytheon (formerly UTRC), Purdue University, and the Connecticut Center for Advanced Technology Inc. will develop a simulation and testing framework to determine the feasibility of using ultrasound to mitigate dirty white spot defects in forged IN 718 turbine parts in the Vacuum Arc Remelting process. Read More
- West Virginia University, University of Buffalo, and Indiana Technology and Manufacturing Companies (ITAMCO) will develop and test hybrid modeling for energy efficient grinding processes for gear manufacturing in collaboration with the industrial partners. Read More
The goal is to develop an instrumented bench scale extrusion kit and platform that can be integrated into education modules (labs, projects, class materials).
The goal of this project is to develop a SM workforce model that leverages existing education and workforce training systems to develop the workforce needed for SM.
Additional Workforce Development Projects
- Tulip Interfaces will develop a set of role-based, simulated work cell exercises to train students on IIoT, analytics, no-code application development and industrial SM applications. The hands-on software-based exercises will be published for dedicated CESMII and can be used to design education curricula cost effectively.
- (SACA) Smart Automation Certification Alliance will partner with Amatrol to develop and pilot a new smart manufacturing micro-credentials program (M Fundamentals, SM Visualization, SM Production Systems and SM Cybersecurity) that addresses the operations and maintenance skills associated with energy efficiency and small-medium business solutions.
- Amatrol will partner with Texas A&M and community colleges to develop low cost hands-on teaching tools based on Augmented/Virtual Reality (AR/VR) and Digital Twins (DT) for a scalable workforce development model for the nation without need for equipment investment. The project increases access to AR/VR based, low cost training resources to accelerate the development of SM skills, increasing the pool of educators with SM skills and making training for incumbent workers more accessible at scale.
- Finger Lakes Community College will partner with Purdue to develop basic, intermediate and advanced resources for educating students at community colleges, baccalaureate institutions and SMMs. It addresses a gap in semi-automation training, connected workers and leverages the benchtop training system developed by Penn State from prior work funded by CESMII. The project will enable co-op training for community colleges and incumbent workforce at small and medium manufacturers.
Real World proof of concept installations delivering value for CESMII members through use of the Smart Manufacturing Innovation Platform, Profiles and Apps Three Application Projects are defined in this Project Round Three Application Project proposals have moved to...