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.


  • Model Requirements on Energy Use Reduction
  • Multi-level model development
  • Data analytics tool for analyzing energy consumption in manufacturing facilities
  • Methodology for sensor selection for fault mitigation and energy savings
  • Supervisory Control for energy-efficient optimal operation and scheduling
  • Secure SM IoT communication protocols aligned with CESMI SM Platform
  • Validation of Sensor Network in the Testbed
  • Dissemination of Results to Industrial Manufacturing Facilities

  • 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


  • 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
Member % Cost Share CESMII % Cost Share Duration
58% 42% 24 months