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Meeting TMS Specialty Congress 2024
Symposium 2nd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2024)
Presentation Title Realtime In-process Monitoring of Porosity via Convolutional Neural Networks During Additive Manufacturing and Laser Welding
Author(s) Bin Zhang, Yung C. Shin
On-Site Speaker (Planned) Yung C. Shin
Abstract Scope This work describes in-process porosity monitoring for additive manufacturing and laser welding processes. A high-speed digital camera was mounted coaxially to the laser beam for in-process sensing of melt-pool data, and deep learning convolutional neural network models were designed to learn melt-pool features to predict the porosity attributes in specimens built by additive manufacturing and laser welding. The convolutional neural network (CNN) models with a compact architecture achieved a classification accuracy of 91.2% for porosity occurrence detection in the direct laser deposition of sponge Titanium powders and presented the predictive capacity for micropores below 100 Ám. 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. In laser welding of 6061 Aluminum alloy, the CNN-based monitoring model achieved a classification accuracy of 96.1% for porosity occurrence detection.
Proceedings Inclusion? Definite: Other


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