| Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm L Scime, J Beuth Additive Manufacturing 19, 114-126, 2018 | 479 | 2018 |
| Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process L Scime, J Beuth Additive Manufacturing 25, 151-165, 2019 | 391 | 2019 |
| A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process L Scime, J Beuth Additive Manufacturing 24, 273-286, 2018 | 334 | 2018 |
| Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation L Scime, D Siddel, S Baird, V Paquit Additive Manufacturing 36, 101453, 2020 | 202 | 2020 |
| Melt pool geometry and morphology variability for the Inconel 718 alloy in a laser powder bed fusion additive manufacturing process L Scime, J Beuth Additive Manufacturing 29, 100830, 2019 | 154 | 2019 |
| A scalable digital platform for the use of digital twins in additive manufacturing L Scime, A Singh, V Paquit Manufacturing Letters 31, 28-32, 2022 | 44 | 2022 |
| Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring Z Snow, L Scime, A Ziabari, B Fisher, V Paquit Additive Manufacturing 61, 103298, 2023 | 38 | 2023 |
| Using coordinate transforms to improve the utility of a fixed field of view high speed camera for additive manufacturing applications L Scime, B Fisher, J Beuth Manufacturing Letters 15, 104-106, 2018 | 18 | 2018 |
| Methods for the expansion of additive manufacturing process space and the development of in-situ process monitoring methodologies LR Scime Carnegie Mellon University, 2018 | 14 | 2018 |
| Localized Defect Detection from Spatially Mapped, In-Situ Process Data With Machine Learning W Halsey, D Rose, L Scime, R Dehoff, V Paquit Frontiers in Mechanical Engineering 7, 767444, 2021 | 13 | 2021 |
| Safety and workflow considerations for modern metal additive manufacturing facilities L Scime, SDV Wolf, J Beuth, S Mrdjenovich, M Kelley Jom 70, 1830-1834, 2018 | 11 | 2018 |
| Integrated control of melt pool geometry and microstructure in laser powder bed fusion of AlSi10Mg SP Narra, L Scime, J Beuth Metallurgical and Materials Transactions A 49, 5097-5106, 2018 | 10 | 2018 |
| Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning Z Snow, L Scime, A Ziabari, B Fisher, V Paquit Additive Manufacturing 78, 103817, 2023 | 8 | 2023 |
| Digital platform informed certification of components derived from advanced manufacturing technologies A Huning, R Fair, A Coates, V Paquit, L Scime, M Russell, K Kane, S Bell, ... Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2021 | 8 | 2021 |
| Development of Monitoring Techniques for Binderjet Additive Manufacturing of Silicon Carbide Structures L Scime, J Haley, W Halsey, A Singh, M Sprayberry, A Ziabari, V Paquit Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2020 | 8 | 2020 |
| Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects L Scime, J Haley, W Halsey, A Singh, M Sprayberry, A Ziabari, V Paquit Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2020 | 6 | 2020 |
| Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1) L Scime, C Joslin, R Duncan, F Brinkley, C Ledford, D Siddel, V Paquit Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Oak Ridge …, 2023 | 4 | 2023 |
| Advancement of Certification Methods and Applications for Industrial Deployments of Components Derived from Advanced Manufacturing Technologies A Huning, A Smith, L Scime, M Russell, A Coates, V Paquit, R Dehoff Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2022 | 4 | 2022 |
| Diagnostic and predictive capabilities of the TCR digital platform L Scime, M Sprayberry, D Collins, A Singh, C Joslin, R Duncan, ... Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2021 | 4 | 2021 |
| Systems and methods for powder bed additive manufacturing anomaly detection LR Scime, VC Paquit, DJ Goldsby, WH Halsey, CB Joslin, MD Richardson, ... US Patent 11,458,542, 2022 | 3 | 2022 |
