Zhenyu (James) Kong
Zhenyu (James) Kong
Smart Manufacturing, Industrial & Systems Engineering, Virginia Tech
Verified email at - Homepage
Cited by
Cited by
Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors
PK Rao, JP Liu, D Roberson, ZJ Kong, C Williams
ASME Trans. Journal of Manufacturing Science and Engineering 137 (6), 061007, 2015
Time Series Forecasting for Nonlinear and Non-Stationary Processes: a Review and Comparative Study
C Cheng, A Sa-Ngasoongsong, O Beyca, T Le, H Yang, ZJ Kong, ...
IISE Transactions 47 (10), 1053-1071, 2015
Image Analysis-based Closed Loop Quality Control for Additive Manufacturing with Fused Filament Fabrication
C Liu, A Law, DM Roberson, ZJ Kong
Journal of Manufacturing Systems 51, 75-86, 2019
Classifying the Dimensional Variation in Additive Manufactured Parts from Laser-Scanned 3D Point Cloud Data using Machine Learning Approaches
MS Tootooni, A Dsouza, R Donovan, PK Rao, ZJ Kong, P Borgesen
ASME Trans. Journal of Manufacturing Science and Engineering 139 (9), 091005, 2017
UseLearn: A novel checklist and usability evaluation method for eLearning systems by criticality metric analysis
A Oztekin, ZJ Kong, O Uysal
International Journal of Industrial Ergonomics 40 (4), 455-469, 2010
Stream-of-variation (SOVA) modeling II: a generic 3D variation model for rigid body assembly in multistation assembly processes
W Huang, J Lin, ZJ Kong, D Ceglarek
ASME Trans. Journal of manufacturing science and engineering 129 (4), 832-842, 2007
Predicting the graft survival for heart–lung transplantation patients: an integrated data mining methodology
A Oztekin, D Delen, ZJ Kong
International journal of medical informatics 78 (12), e84-e96, 2009
In situ investigation into temperature evolution and heat generation during additive friction stir deposition: A comparative study of Cu and Al-Mg-Si
D Garcia, WD Hartley, HA Rauch, RJ Griffiths, R Wang, ZJ Kong, Y Zhu, ...
Additive Manufacturing 34, 101386, 2020
Stream-of-variation modeling—Part I: A generic three-dimensional variation model for rigid-body assembly in single station assembly processes
W Huang, J Lin, M Bezdecny, Z Kong, D Ceglarek
A Machine Learning-based Approach to Prognostic Analysis of Thoracic Transplantations
D Delen, A Oztekin, ZJ Kong
Artificial Intelligence in Medicine 49 (1), 33-42, 2010
An Online Sparse Estimation-based Classification Approach for Real-time Monitoring in Advanced Manufacturing Processes from Heterogeneous Sensor Data
K Bastani, P Rao, ZJ Kong
IISE Transaction 48 (7), 579-598, 2016
Real-Time Identification of Incipient Surface Morphology Variations in Ultraprecision Machining Process
P Rao, S Bukkapatnam, O Beyca, ZJ Kong, R Komanduri
ASME Trans. Journal of Manufacturing Science and Engineering 136 (2), 021008, 2014
A robust ensemble-deep learning model for COVID-19 diagnosis based on an integrated CT scan images database
M Maftouni, ACC Law, B Shen, ZJ Kong, Y Zhou, NA Yazdi
IISE Annual Conference and Expo, 632-637, 2021
Development of a structural equation modeling-based decision tree methodology for the analysis of lung transplantations
A Oztekin, ZJ Kong, D Delen
Decision Support Systems 51 (1), 155-166, 2011
Additive manufacturing of pharmaceuticals for precision medicine applications: A review of the promises and perils in implementation
M Trivedi, J Jee, S Silva, C Blomgren, VM Pontinha, DL Dixon, ...
Additive Manufacturing 23, 319-328, 2018
Online non-contact surface finish measurement in machining using graph theory-based image analysis
MS Tootooni, C Liu, D Roberson, R Donovan, PK Rao, ZJ Kong, ...
Journal of Manufacturing Systems 41, 266-276, 2016
Assessment of Dimensional Integrity and Spatial Defect Localization in Additive Manufacturing (AM) using Spectral Graph Theory (SGT)
P Rao, ZJ Kong, C Duty, R Smith, V Kunc, L Love
ASME Trans. Journal of Manufacturing Science and Engineering, 2015
A graph-theoretic approach for quantification of surface morphology variation and its application to chemical mechanical planarization process
PK Rao, OF Beyca, ZJ Kong, STS Bukkapatnam, KE Case, R Komanduri
IISE Transactions 47 (10), 1088-1111, 2015
Heterogeneous Sensor Data Fusion Approach for Real-time Monitoring in Ultraprecision Machining (UPM) Process Using Non-Parametric Bayesian Clustering and Evidence Theory
OF Beyca, PK Rao, ZJ Kong, STS Bukkapatnam, R Komanduri
IEEE Trans. on Automation Science and Engineering, 2015
Process performance prediction for chemical mechanical planarization (CMP) by integration of nonlinear Bayesian analysis and statistical modeling
ZJ Kong, A Oztekin, OF Beyca, U Phatak, STS Bukkapatnam, ...
IEEE Transactions on Semiconductor Manufacturing 23 (2), 316-327, 2010
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