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

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

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.

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

Repositry supporting two publications on LPBF process monitoring using 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

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

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

This project investigates the relationship between LPBF manufacturing parameters (laser power & scan speed) and acoustic signals. Using data-driven and machine learning methods, we extract acoustic waveform features and build regression models to predict unknown power and speed values. (NUS ME5106 Mini Project 3)

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

Scientific measurement instrument for characterizing the flow behavior of powdered metal feedstock used in additive manufacturing — designed, additively manufactured (LPBF), and CNC-finished at Uniformity Labs (2022-2023). Portfolio piece; design and methodology remain Uniformity Labs proprietary. See NOTICE.md.

Exploring physics-guided machine learning to improve process understanding and prediction in metal additive manufacturing.

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

Mechanical-property datasets, figures, and Colab notebooks for LPBF-built super duplex stainless steel 2507 (UNS S32750), including AS and post-processed conditions.

Transfer-learning (GoogLeNet) pipelines in MATLAB for classifying microstructure/SEM images of LPBF AISI 2507 super duplex stainless steel.

Reverse-engineering and repair of a large industrial tumbler oven for processing powdered metal feedstock for additive manufacturing (LPBF and binder jetting), at Uniformity Labs (2022-2023). Portfolio piece; equipment, drawings, and process know-how remain Uniformity Labs proprietary. See NOTICE.md.