UMD-ARL Alliance for Additive Manufacturing Science
See our ARL Technical Report at: ARL-CR-0866 – Jan. 2022
“The University of Maryland (UMD) and U.S. Army Research Laboratory (ARL) today announced a 5-year $22.8 million cooperative agreement, effective October 1, to accelerate cutting-edge foundational additive manufacturing (AM) materials and technology, aiming at helping the United States Army more efficiently and cost-effectively produce equipment with enhanced capabilities for service members.” See story at: https://eng.umd.edu/news/story/umd-engineering-receives-228m-from-us-army-to-collaboratively-advance-additive-manufacturing-technology
Led by Professor J.C. Zhao, Chair of the Department of Materials Science and Engineering the project will involve six academic departments: Material Science and Engineering, Mechanical Engineering, Aerospace Engineering, Chemical and Biomolecular Engineering, Chemistry and Biochemistry, and College of Information Studies.
“AM processes provide considerable challenges from a big data perspective both in terms of variety (number and types of files), volume (amount of data), and veracity (provenance and trustworthiness of data)”, states Richard Marciano, lead on the “ARL AM Digital Curation and Data Management” work package, which will focus on digital curation, data management, data mining, and the development of a digital asset management system for Additive Manufacturing (AM).
To respond to these challenges we have assembled the following team of experts:
|Bill Underwood, co-Lead
The main processes of 3D printing generate data in multiple formats both open source and proprietary, with potentially terabytes per part. These include (see next image from ARL Additive Manufacturing — shared by Jian Yu, ARL Hybrid AM Team Lead):
- Pre-process (defining requirements and part design):
- CAD data (SOLIDWORKS, AUTOCAD, PTC, etc.). Material: the feedstock information (pdf, excel, txt, jpeg, etc.).
- About 100 to 200 MB of data total for the pre-process per materials/part
- In-process (process optimization (modeling and control):
- Parameter input files native to the machine; simulated and process modeling data (also native to the simulation software packages); in situ process video data and environmental reading (temperature, humidity, etc.).
- Greater than 1 TB of data generated per part printed.
- Post-process (processing / structure / property relations)]:
- Part characteristics, (X-ray CT scans, images, mechanical properties, etc.).
- At least 1TB per part.
Greg Jansen concludes that: “The collection of AM data extends well beyond the input design, to include environmental conditions, equipment settings, and raw materials. It may even extend into the resulting lifecycle and performance of the manufactured item. In all of these concerns there are opportunities for tracking and improvement.”