Zackary K Snow Data Analytics Researcher for Advanced Manufacturing Contact SNOWZK@ORNL.GOV All Publications Demonstrating Advanced Sensors for In-Situ Monitoring Towards Qualification of Nuclear Relevant Components Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning Feature Based Qualification of 17-4PH Stainless Steel to Evaluate Location-Specific Variability in Wire Arc Additive Manufacturing IN-SITU PROCESS MONITORING EVALUATION AND DEMONSTRATION USING ADVANCED CHARACTERIZATION WITH LASER POWDER BED SYSTEMS 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 Driving forces of swelling in laser powder bed fusion additive manufacturing analyzed via in-situ process monitoring Complete Optimization of LPBF Ni-Based Alloys Down-Selected from FY23 Candidate Materials Including, Thermodynamic Modeling, Sample Fabrication and Microstructure Characterization Recent Advances on the Use of In Situ Monitoring as an Nondestructive Evaluation Tool for Additive Manufacturing Processes 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 Data-Driven Optimization of the Processing Window for 316H Components Fabricated Using Laser Powder Bed Fusion Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning Prioritization of Existing Reactor Materials Report Outlining Computed Tomography Strategy and Microscopy Approach to Qualifying AM 316 Materials Preliminary Feasibility of Printing, Microstructure Analysis and Mechanical Performance of a Down Selected Ni Alloy Application of Machine Learning to Monitor Metal Powder-Bed Fusion Additive Manufacturing Processes Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring Key Links Google Scholar Web of Science ORCID Organizations Energy Science and Technology Directorate Manufacturing Science Division Secure and Digital Manufacturing Section Manufacturing Systems Analytics Group
