Luke Scime R&D Associate Staff Scientist Contact SCIMELR@ORNL.GOV All Publications IN-SITU PROCESS MONITORING EVALUATION AND DEMONSTRATION USING ADVANCED CHARACTERIZATION WITH LASER POWDER BED SYSTEMS FY23 Multi-Dimensional Data Correlation Platform: Unified Software Architecture for AMMT Data Management and Processing 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++ Recent Advances on the Use of In Situ Monitoring as an Nondestructive Evaluation Tool for Additive Manufacturing Processes A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network Machine Learning Enabled Sensor Fusion for In-Situ Defect Detection in L-PBF... Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography 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 Report Outlining Computed Tomography Strategy and Microscopy Approach to Qualifying AM 316 Materials Methods for rapid identification of anomalous layers in laser powder bed fusion... Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring Evaluation of AddUp Precision L-PBF Technology for Tooling and Other Industrial Applications Advancement of Certification Methods and Applications for Industrial Deployments of Components Derived from Advanced Manufacturing Technologies A scalable digital platform for the use of digital twins in additive manufacturing Localized defect detection from spatially mapped, in-situ process data with machine learning Digital Platform Informed Certification of Components Derived from Advanced Manufacturing Technologies Report on diagnostic and predictive capabilities of the TCR digital platform Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718 TCR Data Management Plan Report on Progress of Monitoring Techniques for Laser Powder Bed Additive Manufacturing of Metal Structures Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects Pagination Current page 1 Page 2 Next page ›› Last page Last » Key Links Curriculum Vitae Google Scholar ORCID LinkedIn Organizations Energy Science and Technology Directorate Manufacturing Science Division Secure and Digital Manufacturing Section Manufacturing Systems Analytics Group
