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Meeting Materials Science & Technology 2019
Symposium Additive Manufacturing Education
Presentation Title Data Analytics for Metal Additive Manufacturing
Author(s) Amir Mostafaei, Nihal Sivakumar, David Crockett, Anthony D Rollett
On-Site Speaker (Planned) Amir Mostafaei
Abstract Scope Producing defect-free parts with required microstructure and mechanical strength are necessary for the continued rapid growth of metal additive manufacturing (AM). The variation in feedstock powder, processing parameters during a build fabrication and post-processing can contribute to the reduction of defects. We are developing an image-oriented database to consolidate and analyze the data gathered from powder bed metal AM processes to identify the necessary parameters to produce defect-free parts. One issue was that so many different experiments had different parameters that consolidation into a single information file was difficult. The data gathering process had to be designed to include the parameters that were general to all experiments, and a filter search bar was introduced to allow users to search for specific key terms that return specific images. Ultimately, we aim to enable efficient machine learning on a wide range of materials, processes and imaging modalities.
Proceedings Inclusion? Definite: At-meeting proceedings


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Additive Manufacturing in Capstone Design Projects at Materials Innovation Guild @ University of Louisville
Additive Manufacturing in Making Powder Metallurgy Filters – Student Experiences in Manufacturing Laboratory
Data Analytics for Metal Additive Manufacturing
Engineering Better Additive Manufacturing Education: Leveraging Research-based Approaches to Prepare the Future Workforce
Free-form Additive Manufacturing Lab
Hands-on Additive Manufacturing Education for Undergraduate Materials Engineers
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