Data Modeling for Smart Aerospace Additive Manufacturing

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.

TECHNICAL APPROACH: 

• 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.

KEY TASKS AND MILESTONES:

• 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