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

Real-Time Monitoring and Quality Assurance for Laser-Based Directed Energy Deposition: Integrating Coaxial Imaging and Self-Supervised Deep Learning Framework

一个Python3程序，可以监视系统内各个或者某个进程的资源占用，帮助你找出问题，揪出高占用或者自动启动的进程

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

Monitoring of direct energy deposition process using deep-net based manifold learning and co-axial melt pool imaging

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 is a project to analyze files to generate procmon logs,windump pcap,and extact codechunks and analyze

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

Programming in Python

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

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

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