In Situ Quality Monitoring in Direct Energy Deposition Process using Co-axial Process Zone Imaging and Deep Contrastive Learning
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Updated
Aug 17, 2022 - Python
In Situ Quality Monitoring in Direct Energy Deposition Process using Co-axial Process Zone Imaging and Deep Contrastive Learning
Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance
Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process
Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions
This project was made in Alkimat during a computer vision master's course on UFSC (Universidade Federal de Santa Catarina). The scope of the development was to segment a printed layer on an image shoot from a Laser Powder Bed Fusion (LPBF) machine developed in Alkimat.
Repositry supporting two publications on LPBF process monitoring using acoustic emissions
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
Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
Monitoring Of Laser Powder Bed Fusion Process By Bridging Dissimilar Process Maps Using Deep Learning-based Domain Adaptation on Acoustic Emissions
GO-MELT: GPU-Optimized Multilevel Execution of LPBF Thermal simulations
Qualify-As-You-Go Sensor Fusion, Process Zone Signatures and Deep Contrastive Learning for Multi-Material Composition Monitoring in LPBF Process
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