Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
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Updated
Nov 9, 2023 - Python
Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
Repositry supporting two publications on LPBF process monitoring using acoustic emissions
Monitoring Of Laser Powder Bed Fusion Process By Bridging Dissimilar Process Maps Using Deep Learning-based Domain Adaptation on Acoustic Emissions
Slide deck for the talk "Small Language Models - running a ChatGPT equivalent on your own laptop"
A "small" language model using Markov Chains for text generation
Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions
Demonstrations of slm analysis
Lite Korean language model
Container for slm, slm_demo repos
CPU and GPU (using HIP) implementations of phase pattern generators for use with spatial light modulators
Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance
🔌 Slm language support for VS Code.
Sensor selection for process monitoring based on deciphering acoustic emissions from different dynamics of the Laser Powder Bed Fusion process using Empirical Mode Decompositions and Interpretable Machine Learning
Streamline mapping of landscape structure
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