Data Curation, Fusion, and Analysis for Metals Additive Manufacturing Pipeline

Research

In order to harness the immense amount of data obtained from sensors within the additive manufacturing (AM) machines, build planning, and post-build imaging analysis throughout the entire AM pipeline, this project aims to compile and consolidate existing multi-modal data sets and establish protocols to address future data generation.

By addressing data storage capacity, particularly for large CT data sets; file naming conventions; and data retrieval standards, Carnegie Mellon researchers will have better access to data needed for applying machine learning tools to advancing AM technology.

Principal Investigator
Anthony Rollett
Additional Investigator
Amir Barati Farimani
Related Links
ARL AI-Enabled Additive Manufacturing
Research Areas
AI/ML