Obserwuj
Luke Scime
Luke Scime
Zweryfikowany adres z ornl.gov
Tytuł
Cytowane przez
Cytowane przez
Rok
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
4372018
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
3472019
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
3022018
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
1792020
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
1392019
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
382022
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
282023
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
152018
Methods for the expansion of additive manufacturing process space and the development of in-situ process monitoring methodologies
LR Scime
Carnegie Mellon University, 2018
142018
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
112018
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
92021
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
92018
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 Lab.(ORNL), Oak Ridge, TN (United States), 2020
82020
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 Lab.(ORNL), Oak Ridge, TN (United States), 2021
62021
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
52020
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
42023
Diagnostic and predictive capabilities of the TCR digital platform
L Scime, M Sprayberry, D Collins, A Singh, C Joslin, R Duncan, ...
Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2021
42021
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
32023
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
32022
Utilizing a dynamic segmentation convolutional neural network for microstructure analysis of additively manufactured superalloy 718
S Taller, L Scime, K Terrani
Microscopy and Microanalysis 27 (S1), 3110-3112, 2021
32021
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20