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Meeting MS&T24: Materials Science & Technology
Symposium Additive Manufacturing: Artificial Intelligence and Data Driven Approaches
Sponsorship TMS: Additive Manufacturing Committee
Organizer(s) Eric Clough, HRL Laboratories
Mohsen Asle Zaeem, Colorado School of Mines
Bo Shen, New Jersey Institute of Technology
Xiaopeng Li, University of New South Wales
Scope Additive manufacturing is a continuously growing field with the potential to revolutionize the manufacture of critical engineering components. Developing an understanding of the complex parameter space involved to reliably produce high quality parts is particularly challenging due to the large variable space that must be considered including machine, material, and part geometry specific settings. To improve part quality, large amounts of data are collected in-situ utilizing nondestructive methods such as thermal, acoustic, ultrasonic, and visual inspection. Further, manufactured components are investigated postproduction using a range of characterization methods to quantify the grain structure, texture, porosity, and secondary phase content of the materials. Organizing and analyzing this volume of data has proved difficult, especially as it relates to identifying quantifiable and actionable processing improvements on an industrially relevant scale.

This symposium aims to examine collection, management, and analysis of data that can be used to expedite and improve the additive manufacturing process. Academic analysis is of interest, however this symposium is particularly interested in identifying methods that can be introduced into the growing additive industry and digital manufacturing framework.

Specific topics of interest include artificial intelligence and machine learning approaches focused on leveraging process and inspection data to improve additive manufacturing, including but not limited to digital twins, nondestructive evaluation, data management, and accelerated material and/or part qualification.

Abstracts Due 05/15/2024

Accelerating Engineering Design through Scientific AI and Adaptive Sampling
AI-Powered Prediction of the Flash Onset in Oxides
Chemical Composition Based Machine Learning and Multi-Physics Model to Predict Defect Formation in Additive Manufacturing
Prediction of Mechanical Properties of AlSi10Mg by Laser Powder Bed Fusion Using In Situ Processing Data with Image-Based Transfer Learning

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