A supervisory control system targeting energy efficiency gains in the manufacturing facility will be developed and demonstrated. This task will leverage UTRC scalable control architectures, which have been demonstrated to yield >20% energy savings in the buildings domain. The proposed optimization framework will be based on the holistic view of the manufacturing system, including heterogenous parallel production lines (producing the same component) and interaction of the manufacturing system with the building ancillary equipment.

• This subtask will optimize energy consumption based on the cumulative load of the integrated facility and across production lines. An integrated evaluation and optimization approach will be developed to effectively manage energy usage in the facility without compromising product quality and throughput time. Energy efficient planning and scheduling system will be developed and implemented to minimize idle energy and power peak through a system level
approach, which considers the interactions of the manufacturing system. (subtask 4.1)
• This subtask will design control structures targeting the energy reduction requirements defined in Task 1. Optimal control strategies will be selected based on performance, model (or data) requirements and complexity. The supervisory controller will utilize the process models developed in Task 2, as part of the optimization algorithm, to determine optimal process references or variables (such as set points, feed-rate, speed etc.) that meet a pre-defined
productivity and energy-based cost function. The process models will be adapted on-line to improve their accuracy with respect to machine parameters or operation changes. The algorithm will also incorporate constraints for process and product quality. (subtask 4.2)
• To avoid deterioration in product quality or damage to machines, advanced diagnostics and control algorithms will be implemented to quickly detect faults and isolate them, and mitigate the propagation of their effects. The focus of this task is to develop a fault tolerant control (FTC) module that adapts the supervisory level control algorithm to the faulty system, resulting in little or no degraded performance. Depending on the type of fault, the proposed optimization-based controller will automatically respond by modifying its objective function or constraints. The FTC module will also serve as an advisory system to the operation manager, presenting consequences of the system’s faulty state, action taken and the corresponding performance in terms of energy savings and constraint (quality) satisfaction. (subtask 4.3)
• Energy aware planning and scheduling is imperative to effectively manage energy
consumption in smart manufacturing operations. In many cases, start-up of manufacturing
equipment is energy intensive and equipment operation at part-load condition is usually
inefficient. Effective energy strategies will be developed and implemented to minimize idle
energy and power peak through holistic system approach, which considers subsystem
interactions in the manufacturing system. (subtask 4.4)

 

Clean Energy Smart Manufacturing Innovation Institute

Investigators

 

George Bollas

george.bollas@uconn.edu

Dr. George Bollas is the Director of the Institute for Advanced Systems Engineering (IASE) at the University of Connecticut. He is an Associate Professor with the Department of Chemical and Biomolecular Engineering at the University of Connecticut, a process design expert and winner of the prestigious NSF CAREER Award and the ACS PRF DNI Award. He received B.E. and Ph.D. degrees from the Aristotle University of Thessaloniki in Greece and then worked as a postdoctoral research associate at the Chemical Engineering Department of MIT. At UConn, he is leading efforts to develop and explore novel system representations (steady state and dynamic models) of thermal fluid systems (TFS) in equation-oriented environments that allow system dynamic optimization, sensitivity and uncertainty analysis, fault detection and optimal control. Dr. Bollas is also the director of the Process Design Simulation and Optimization Laboratory (PDSOL). The lab pursues a balanced approach to experimentation guided by robust modeling and simulation of chemical processes, including experimental design, process scaling and control.

 

Changsheng Guo

Dr. Changsheng Guo has been with the United Technologies research Center (UTRC) since 1999 where he is currently an Associate Director and Project Leader with the United Technologies Research Center (UTRC) where he leads projects in developing and implementing advanced manufacturing technologies for aerospace manufacturers.
Changsheng and team have developed software tools based on manufacturing process physics. The software tools for machining and grinding have been implemented in production and demonstrated more than 50% cycle time reduction.

Changsheng received his Ph.D. in mechanical engineering and MBA from University of Massachusetts, a Master's degree in manufacturing engineering Northeastern University in China. Before joining UTRC, Changsheng was a research Fellow at the University of Massachusetts, the Technical Director of a ceramic machining company, and an assistant professor in Manufacturing at Northeastern University in Shenyang, China.

Dr. Guo’s research focus has been on the fundamentals and applications of manufacturing processes such as machining, grinding, and metal forming. He has more than 80 published papers, co-authored one book, and 20 patents. Changsheng is a member of the CIRP (International Academy for Production Engineering), a Fellow of SME, an associate editor for the Journal of Machining Science and Technology. Changsheng received numerous awards such as the prestigious F. W. Taylor Medal of CIRP and the ASME Blackall Award.

 

Shalabh Gupta

shalabh.gupta@engr.uconn.edu

Dr. Gupta’s research is focused on the science of autonomy with emphasis on two key areas: Data Analytics and Networked-Intelligent systems. Application examples include complex human-engineered systems such as a network of unmanned vehicles, medical robotics, distributed sensor networks, power grids, aircraft control systems, hybrid vehicles, etc. Some key research areas include machine perception, information fusion, distributed learning, adaptive decision & control in presence of uncertainties, cooperative tasking and adaptive navigation of unmanned vehicles, intelligent sensor networks for Intelligence, Surveillance & Reconnaissance (ISR) operations, and fault diagnosis & prognosis in networked-control systems. In essence, his research is centered around the essential characteristic of cyber-physical systems that links the domain of underlying system dynamics with the domain of information & control.

 

Peter Luh

peter.luh@uconn.edu

Peter Luh received his BS from National Taiwan University, MS from MIT, and PhD from Harvard University. He has been with the Department of Electrical and Computer Engineering at the University of Connecticut, Storrs, Connecticut since 1980, and is the SNET Professor of Communications & Information Technologies. He was the Director of Taylor L. Booth Engineering Center for Advanced Technology (1997-2004); and the Head of the Department of Electrical and Computer Engineering (2006-2009). He is a Fellow of IEEE, and a member of the Periodicals Committee of IEEE Technical Activities Board. He was the founding Editor-in- Chief of the IEEE Transactions on Automation Science and Engineering, and an EiC of IEEE Transactions on Robotics and Automation. He is the recipient of the 2013 Pioneer Award of the IEEE Robotics and Automation Society for his pioneering contributions to the development of near-optimal and efficient planning, scheduling, and coordination methodologies for manufacturing and power systems.

 

Matt Reiling

Engineering Director with over 20 years of experience in the orthopaedic market, guiding Medical Device products from concept through launch. Experienced leader building a long-term strategic plan while executing priorities to meet the short-term needs of the site. He developed site engineering structures for six sites using a data-based approach and current best practices and recommended new site structures to maximize engagement and leverage individual site structures to create a cluster approach to managing technical programs. Some of his other experience includes building a technical team with a strong focus on developing existing talent and future leadership pipeline across multiple sites, leading a site in delivering over $16mm in cost savings over the past 5 years, strengthening site stability through expansion of products manufactured at site, 45% of products less than 5 years old, and establishing long term competitiveness of site through development of cost improvement philosophy.