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Meeting MS&T24: Materials Science & Technology
Symposium Standards for Data Science in Additive Manufacturing
Sponsorship TMS: Additive Manufacturing Committee
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Shengyen Li, National Institute of Standards and Technology
Donna P. Guillen, Idaho National Laboratory
Mark R. Stoudt, National Institute of Standards and Technology
John S. Carpenter, Los Alamos National Laboratory
Tyler C. Lebrun, Sandia National Laboratories
Mahdi Jamshid, ASTM International
Soumya Nag, Oak Ridge National Laboratory
Bo Shen, New Jersey Institute of Technology
Scope The symposium aims to bring together materials scientists and experts in data science to discuss and establish standards for data science applications in additive manufacturing (AM). The event will focus on important topics such as verification, validation, uncertainty quantification, data curation, and data repositories, as well as case studies highlighting successful implementation. The symposium includes presentations, panel discussions, and networking sessions.

Proposed Topics:
Topic Area 1: Motivation and Applications of Data Science in Additive Manufacturing
• Overview of standards developed by other professional organizations
• Importance of data science standards in additive manufacturing
• Applications of data science in additive manufacturing
• Data usage for certification, qualification aspects of AM parts
Topic Area 2: Verification, Validation, Uncertainty Quantification and Propagation
• Understanding verification and validation concepts in data science
• Best practices for verifying and validating data science models in additive manufacturing
• Overview of uncertainty quantification methods and techniques
• Incorporating uncertainty analysis in data-driven manufacturing processes
• Addressing uncertainties in material properties and process parameters
Topic Area 3: In Situ, Ex Situ Data and Quality Assurance
• Importance of data curation and quality assurance in additive manufacturing
• Techniques for data preprocessing, cleaning, and normalization
• Ensuring data integrity and reliability for accurate modeling and analysis
Topic Area 4: FAIR Data Repositories and Sharing Standards
• Overview of existing data repositories in materials science and additive manufacturing
• Data sharing standards and protocols for collaborative research
• Challenges and opportunities in creating open and accessible data repositories

Additional forums to build upon the energy created by this symposium include:
1. Panel Discussion: Bridging the Gap between Materials Science and Data Science. A panel of experts from materials science and data science fields will identify common challenges and opportunities, as well as strategies for effective collaboration between standards organizations.
2. Poster Session: Participants can showcase their research and projects related to data science in additive manufacturing through posters, fostering networking and knowledge exchange.
3. Industry Showcase: An opportunity for industry representatives to present their experiences and case studies on implementing data science standards in additive manufacturing.
4. Interactive Workshops: Optional workshops can be organized to provide hands-on training on specific data science tools, techniques, or software relevant to additive manufacturing.
5. Networking Sessions: Dedicated networking breaks and a closing reception to encourage interactions, collaborations, and exchange of ideas among participants.

Abstracts Due 05/15/2024
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE
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