PROJECT LEAD:  Honeywell

PARTNERS: MORF3D, University of California Los Angeles, University of Southern California, Missouri University Science &Technology, Identify 3D, Sentient Science Corp, Raytheon

PROBLEM STATEMENT: Challenges in contextualization of the enormous amounts of data in a meaningful timeframe in data modeling including gathering of right data; real-time data collection scalability; application of right machine learning tools; interoperability; interactions between edge and cloud; connectivity of smart factory to office; office/factory to cloud; cloud services which connect back with the office; factory and the worker/process engineer.

PROJECT GOAL: The goal of this project is to develop technologies on data modeling, machine learning and data-centric analytics for smart Aerospace additive manufacturing and to implement these innovations using data from working Aerospace manufacturing facilities.


  • Accelerate the implementation of critical data modeling for machine learning and data-centric analytics into Smart Manufacturing (SM) technologies for aerospace. The project will implement these innovations using data from working Aerospace manufacturing facilities.


  • Identify and collect available data sources within smart manufacturing
  • Accurately capture and predict defects through data modeling originating from the process of the manufacturing plant
  • Develop, refine, and integrate data analytics and machine learning into SM DMLS system
  • Set up the edge-cloud interoperability and establish a workflow to demonstrate the ML toolkit
  • Demonstration OF D2ML technologies using two working Aerospace manufacturing sites
  • Financial tracking, reporting, and project cost performance

  • Improve AM process development monitoring and build efficiencies reducing the need for multiple development iterations to establish acceptable build parameters
  • Energy usage will be reduced as development cycles are eliminated
  • Additive Manufacturing (AM) as an affordable alternative to more traditional, energy-intensive part manufacturing processes, reducing overall US manufacturing energy consumption
  • This program will enable the large data sets currently being created by AM equipment to be utilized to develop actionable insights and make affordable AM part build processes


  • D2ML addresses secure, scalable, and interoperable on premise, edge, and off-premise cloud-technology integration
  • Application across aerospace industry
  • Help new AM startups accelerate their AM product expansion through SM technologies
Member % Cost Share CESMII % Cost Share Duration
36% 64% 18 months