About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
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Symposium
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Additive Manufacturing: Advanced Characterization with Synchrotron, Neutron, and In Situ Laboratory-scale Techniques
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Presentation Title |
In-process Monitoring of Porosity in Additive Manufacturing |
Author(s) |
Bin Zhang, Shunyu Liu, Yung C. Shin |
On-Site Speaker (Planned) |
Yung C. Shin |
Abstract Scope |
This work describes in-process porosity monitoring for additive manufacturing processes based on deep learning and a real time weld pool monitoring system. A high-speed digital camera was mounted coaxially to the laser beam for in-process sensing of melt-pool data, and convolutional neural network models were designed to learn melt-pool features to predict the porosity attributes in built specimens during additive manufacturing. The convolutional neural network (CNN) models with a compact architecture, part of whose hyperparameters were selected through cross-validation analysis, achieved a classification accuracy of 91.2% for porosity occurrence detection in the direct laser deposition of sponge Titanium powders and presented predictive capacity for micro pores below 100 µm. For local volume porosity prediction, the model also achieved a root mean square error of 1.32% and exhibited high fidelity for both high porosity and low porosity specimens. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |