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About this Symposium

Meeting MS&T26: Materials Science & Technology
Symposium Additive Manufacturing: Equipment, Instrumentation and In-Situ Process Monitoring
Sponsorship TMS: Additive Manufacturing Committee
Organizer(s) Samantha Webster, Colorado School of Mines
Benjamin D. Bevans, University of Oklahoma
Jihoon Jeong, Texas A&M University
Yash Parikh, EOS of North America, Inc.
Scope The purpose of this symposium is to highlight advances made in Additive Manufacturing (AM) equipment and instrumentation which supports in-situ process monitoring techniques. Research can be focused on the development of an innovative measurement system at the laboratory scale or an existing monitoring solution for production applications.

Suitable topics for this symposium include, but are not limited to, the following:

1. New technologies or improvements in AM equipment.
• Faster, more repeatable, or innovative delivery of feedstock
• Improvements in build chamber environment
• Innovations in energy transfer to material

2. In-situ monitoring technologies and process control algorithms.
• Multi-sensor monitoring and incorporation into process control
• Measurement methods and instrumentation to observe high-speed melt pool phenomena, material composition, and defect formation
• Closed-loop and feed-forward control algorithm development, or predictive model implementation on existing hardware

3. In-situ monitoring data reduction, processing, and analysis.
• Fusion of data from multiple sensors
• Methods to map in-situ data to final component geometry
• Application of AI and machine learning to in-situ data

Abstracts covering additive manufacturing research and standards development will also be welcomed.

Abstracts Due 05/19/2026

PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE


AMDiffusion: Domain-Adaptive Diffusion Modeling for Causal Data Fusion in Additive Manufacturing
Beyond Deep Learning: A Bayesian-Optimized Computer Vision Framework for Rapid Spatter Detection and Tracking in Laser Powder Bed Fusion
Designing sensor systems for anomaly and flaw detection in laser powder bed fusion additive manufacturing
Hybrid Feedforward-Feedback Process Control of Laser Powder Bed Fusion
K2: An Open Architecture Wire-Laser Directed Energy Deposition Testbed for Advanced Control Strategy Development
Large Language Models for In-Situ Interpretation of Defect Signatures in Powder Bed Fusion
Rapid Modeling and Prediction of Thermal Strain in Laser Powder Bed Fusion
Self-Sensing of 3D-Printed Materials by Measuring the Inductance, Resistance and Capacitance
Smoke, Mirrors, and Melt Pools: An assessment of commercial PBF-LB In-Situ Process Monitoring Solutions


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