Project Lead: University of Connecticut

Partners: Johnson & Johnson, United Technologies Research Center, Connecticut Center for Advanced Technology


Problem Statement:  Coordinated utilization of systems engineering, modeling, advanced controls, and data analytics will enable energy efficiency improvement in the precision machining and hybrid manufacturing of metals/alloys to support cross-industry platforms, including aerospace and orthopedics.

Project Goal: The objective of this effort is to mitigate energy waste in manufacturing facilities, and specifically subtractive precision manufacturing, using model-based systems engineering principles.


Technical Approach:

  • Integration of the following modules in the Smart Manufacturing Platform for precision machining:

  • platform-based systems engineering to enable requirements formalization and reusability,

  • multi-level, heterogeneous and hybrid modeling of manufacturing and ancillary equipment,

  • predictive analytics for anomaly detection using sensory information and data analytics,

  • context-driven supervisory control architectures enabling model/control interoperability,

  • scheduling of manufacturing operations to maximize energy savings,

  • big data analytics, reduction and secure IoT communication protocols.

Key Tasks/Milestones:

  • Model Requirements on Energy Use Reduction (M4)

  • Multi-level model development (M12)

  • Data analytics tool for analyzing energy consumption in manufacturing facilities (M12)

  • Methodology for sensor selection for fault mitigation and energy savings (M16)

  • Supervisory Control for energy-efficient optimal operation and scheduling (M18)

  • Secure SM IoT communication protocols aligned with CESMI SM Platform (M20)

  • Validation of Sensor Network in the Testbed (M24)

  • Dissemination of Results to Industrial Manufacturing Facilities (M24)

Potential Impact:

  • 25% reduction in energy usage, operations costs and downtime would result in about $55M in annual savings at J&J manufacturing base

    50% reduction in manufacturing energy consumption in non-optimized UTC manufacturing facilities

    Architecture systematically diagnoses inefficiencies and executes on improvements to optimize energy consumption and operational efficiency for precision machining equipment

    Partnering with manufacturers to incorporate sensing and diagnosing elements will substantially impact energy efficiency of metals manufacturing

Benefit to CESMII:

  • Technology incorporated into the SM Platform, made available to members

  • Incorporate the smart manufacturing elements, into equipment specifications that are reflected in RFQ (Requests for Quotation) by major equipment users

  • Benefits for small and mid-sized companies as equipment suppliers integrate SM technology in their products

  • Enable Industry working groups to incorporate smart manufacturing approach into ISO standards

Project Cost Share and Duration:

Project Duration: 24 months, CESMII Cost Share: 42%, Member Cost Share: 58%