Vincent Paquit Section Head, Secure & Digital Manufacturing Contact 865.241.1789 | PAQUITVC@ORNL.GOV All Publications A Co-Registered In-Situ and Ex-Situ Dataset from Wire Arc Additive Manufacturing process FY23 Multi-Dimensional Data Correlation Platform: Unified Software Architecture for AMMT Data Management and Processing Review of In Situ Sensing for Directed Energy Deposition for Industrial Part Quality Assessment FY 2024 Multidimensional Data Correlation Platform: Unified Software Architecture For Advanced Materials And Manufacturing Technologies Data Management And Processing Peregrine Software Development: Report on the Code Conversion From Python to C++ FY 2024 Multidimensional Data Correlation Platform Data Management Infrastructure Progress: Materials Laboratory Denoising diffusion probabilistic models for generative alloy design Recent Advances on the Use of In Situ Monitoring as an Nondestructive Evaluation Tool for Additive Manufacturing Processes Deep-learning based artificial intelligence tool for melt pools and defect segmentation Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in L-PBF... A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts Neural network-based single material beam-hardening correction for X-ray CT in Additive Manufacturing Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography IN-SITU MONITORING ASSISTED LARGE-SCALE ADDITIVE MANUFACTURING OF MILD STEEL AND 316L ALLOYS FOR NUCLEAR APPLICATION Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning Methods for rapid identification of anomalous layers in laser powder bed fusion... Process Optimization... Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction Towards generic memory forensic framework for programmable logic controllers Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction Advancement of Certification Methods and Applications for Industrial Deployments of Components Derived from Advanced Manufacturing Technologies High Throughput Deep Learning-Based X-ray CT Characterization for Process Optimization in Metal Additive Manufacturing Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing A scalable digital platform for the use of digital twins in additive manufacturing Pagination Current page 1 Page 2 Page 3 Next page ›â¶Äº Last page Last » Key Links Curriculum Vitae Organizations Energy Science and Technology Directorate Manufacturing Science Division Secure and Digital Manufacturing Section
