Energy Management Systems for Subtractive and Additive Precision Manufacturing

Project Lead: University of Connecticut
Partners: Johnson & Johnson, United Technologies Research Center, Connecticut Center for Advanced Technology

Member % Cost Share: 58%
CESMII % Cost Share: 42%
Duration: 24 Months

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.

Deliverables/Outcomes/SM Marketplace

  • Developed complete modeling framework for smart precision machining

  • Developed varying fidelity models for all precision machining components

  • Developed complete machine learning models for audio, video, and vibration data

  • Validated models with data from the Connecticut Center for Advanced Technology (CCAT)

  • Developed a data analytics tool for analyzing energy consumption in manufacturing facilities

  • Predictive models for precision machining

  • Process health monitoring application for machining tool wear

  • Optimal CNC machine job shop scheduling application

  • Tool wear aware fault-tolerant control system

  • Experimental database of tool wear data

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


  • 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

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