Luke R Scime R&D Associate Staff Scientist Contact scimelr@ornl.gov | 865.574.3343 All Publications A Data-Driven Framework for Direct Local Tensile Property Prediction of Laser Powder Bed Fusion Parts Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography 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... Report on diagnostic and predictive capabilities of the TCR digital platform... Digital Platform Informed Certification of Components Derived from Advanced Manufacturing Technologies... Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superall... TCR Data Management Plan... Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects... Development of Monitoring Techniques for Binderjet Additive Manufacturing of Silicon Carbide Structures... Report on Progress of Monitoring Techniques for Laser Powder Bed Additive Manufacturing of Metal Structures... Layer-Wise Anomaly Detection and Classification for Powder Bed Additive Manufacturing Processes: A Machine-Agnostic Algorithm... Monitoring for additive manufacturing technologies: Report on progress, achievements and limitations of monitoring techniques... Viability of data analytics to ascertain component performance for additive manufacturing... 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
