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About this Abstract
Meeting	MS&T22: Materials Science & Technology
Symposium	Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title	B-5: Using Computer Vision and Machine Learning to Characterize Melt Pool Geometry in Additive Manufacturing
Author(s)	Han Chien, Bo Lei, Bryan Webler, Elizabeth Holm
On-Site Speaker (Planned)	Han Chien
Abstract Scope	The dimension of the melt pool determines the initial geometry of the material deposition in the process of laser powder bed fusion (LPBF). It is crucial for quality control to identify the microstructural features by using computer vision techniques. A model is built for deep learning on image segmentation which can recognize the boundaries between the heat affected zone and the unaffected base metal. The model is able to identify the shape and size of the heat affected zone and thus the dimension of the melt pool can be calculated. This study gives insight into the image segmentation of the melt pool geometry and contributes to the dimension measurement of it.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Addressing Data Needs for High Temperature Material Processing with Natural Language Processing

AI Driven Microscopic Analysis to Predict the Local Structure in Zirconia Ceramics

AI/ML-Driven Multi-Scale Modeling and Design of Structural Materials

B-5: Using Computer Vision and Machine Learning to Characterize Melt Pool Geometry in Additive Manufacturing

Comparison of Data Driven and Physics-informed Machine Learning Models for Temperature Prediction of Shear Assisted Processing and Extrusion

Composition and Property Prediction of Polymer-derived Silicon Oxycarbides

Computational and Machine Learning Studies of DNA-templated Dye Aggregate Design

Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys

Graph Neural Network Modeling of Deforming Polycrystals

High-throughput Machine Learning Experiments with Graph Neural Networks for Predicting Abnormal Grain Growth in Polycrystalline Materials

Large Scale Atomistic Simulation of the B1-B4 Phase Transition of GaN with the Machine Learning Potential

Machine Learning Based Prediction of Cation Distribution in Complex Spinel Oxides as a Function of Processing Temperature

Machine Learning for Joint Quality Performance-determining Relationship between Intermetallic Properties and weld Microstructure of Al/steel Resistance Spot Welds

Microstructure Characterization and Reconstruction by Deep Learning Methodology

Unraveling the Process Fundamentals of Additive Friction Stir Deposition by Integrating Physics Simulation with Data-driven Approaches

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