GO-MELT: GPU-Optimized Multilevel Execution of LPBF Thermal simulations

Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process

In Situ Quality Monitoring in Direct Energy Deposition Process using Co-axial Process Zone Imaging and Deep Contrastive Learning

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.

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

Monitoring Of Laser Powder Bed Fusion Process By Bridging Dissimilar Process Maps Using Deep Learning-based Domain Adaptation on Acoustic Emissions

Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance

Repositry supporting two publications on LPBF process monitoring using acoustic emissions

Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring

Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions

Qualify-As-You-Go Sensor Fusion, Process Zone Signatures and Deep Contrastive Learning for Multi-Material Composition Monitoring in LPBF Process