Energy consumption in a manufacturing facility is comprised of direct energy used by various machining operations and indirect energy consumed by activities to maintain the environmental conditions (e.g., lighting, heating and ventilation). Reducing the energy consumption in a manufacturing facility requires sensors to monitor the energy usage patterns (“energy profiles”) and a concomitant data analytics process for correlating them with the activities being performed. This task will design, integrate and install a sensor network at the testbed facility to support the manufacturing and building management system for measuring and identifying the context of energy usage. This information will be used in production planning and scheduling to optimize energy usage and reduce energy cost. Specific activities of this task are described in the following:

• A suite of embedded controllers and energy analyzers with capabilities for energy (sub-) metering, power factor and power quality logging, web-based energy monitoring and wireless data acquisition will be selected. Subsequently, optimal locations will be chosen for monitoring the direct and indirect energy consumption using the energy consumption models developed in Task 2, for the test bed facility to maximize a measure of the information matrix (e.g., trace, determinant) or mutual information-based metrics, subject to a constraint on the cost. The Energy Management Systems for Subtractive & Additive Precision Manufacturing
01/31/2018 Roadmap Project Proposal 7 sensor network will be selected based on its cost, ease of programming and operation, computational capabilities and standardized communication with the SM Platform and virtual data analytics entities. Existing sensors, soft sending and inference of information will be prioritized to reduce cost and uncertainty growth. (subtask 3.1)
• Develop data science-based analytics to correlate energy usage and the actual physical activities in the test bed facility, including equipment breakdowns and degradations. This analysis can be used to generate an energy profile of the machine or process stage when working on a product. The energy profile will illustrate the value-added energy, and the nonvalue-added energy when production is not occurring, and serve as proactive decision support to supervisors when it is linked to planning and scheduling. (subtask 3.2)
• The models developed in Task 2 will be enhanced to include the sensors selected in subtask 3.2 to exercise data analytics by means of model-in-the-loop experiments on machining and 3D printing operations and anomalies (e.g., process faults). (subtask 3.3)

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.

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.

Krishna Pattipati

krishna.pattipati@uconn.edu

Dr. Krishna Pattipati received the B. Tech. degree in electrical engineering with highest honors from the Indian Institute of Technology, Kharagpur, in 1975, and the M.S. and Ph.D. degrees in systems engineering from UConn, Storrs, in 1977 and 1980, respectively. He was with ALPHATECH, Inc., Burlington, MA from 1980 to 1986. He has been with the department of Electrical and Computer Engineering at UConn, where he is currently the Board of Trustees Distinguished Professor and the Chair Professor in Systems Engineering. Dr. Pattipati’s research activities are in the areas of proactive decision support, uncertainty quantification, smart manufacturing, autonomy, knowledge representation, and optimization-based learning and inference. A common theme among these applications is that they are characterized by a great deal of uncertainty, complexity, and computational intractability. He is a cofounder of Qualtech Systems, Inc., a firm specializing in advanced integrated diagnostics software tools (TEAMS, TEAMS-RT, TEAMS-RDS, TEAMATE), and serves on the board of Aptima, Inc.

Dr. Pattipati was selected by the IEEE Systems, Man, and Cybernetics (SMC) Society as the Outstanding Young Engineer of 1984, and received the Centennial Key to the Future award. He has served as the Editor-in-Chief of the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS–PART B from 1998 to 2001. He was co-recipient of the Andrew P. Sage Award for the Best SMC Transactions Paper for 1999, the Barry Carlton Award for the Best AES Transactions Paper for 2000, the 2002 and 2008 NASA Space Act Awards for “A Comprehensive Toolset for Model-based Health Monitoring and Diagnosis,” and “Real-time Update of Fault-Test Dependencies of Dynamic Systems: A Comprehensive Toolset for Model-Based Health Monitoring and Diagnostics”, and the 2003 AAUP Research Excellence Award at UCONN. He is an elected Fellow of IEEE and of the Connecticut Academy of Science and Engineering.