My research is in process monitoring for Directed Energy Deposition, and Additive Manufacturing process. Those who are familiar know that this process is currently lacking quality, so here is where my research enters.

I have developed a 3D voxel visualizer to perform a quality inspection of the printed parts, and have a non-destructive analysis.

Lately, I have been employing Machine Learning through CNNs to predict several process variables using a coaxial camera that points to the melt pool, where the metal gets molten.

Also, I am collaborating with Professor Samuel Oliveira in analyzing microbial communities in microfluidic chips with microscopy imaging.

Publications:

• (2023) Real-time prediction of deposited bead width in L-DED using Semi-Supervised Transfer Learning
• (2023) A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition
• (2021) A novel melt pool mapping technique towards the online monitoring of Directed Energy Deposition operations
• (2020) Development of a low cost computer program for integrating g-codes on manufacture and repair of metal parts by Directed Energy Deposition (DED)