Robert Gao

Department Chair, Mechanical and Aerospace Engineering Professor, Mechanical and Aerospace Engineering
Develops multi-physics sensing and stochastic modeling methods for improving observability in dynamical systems
Phone Number: (216) 368-6045 Email: robert.gao@case.edu

Education

Ph.D., Mechanical Engineering, Technical University of Berlin, Germany, 1991
M.S., Mechanical Engineering, Technical University of Berlin, Germany, 1985
B.S., Mechanical Engineering, Central Academy of Arts and Design, Beijing, China, 1982

Awards and Recognitions

2023, Milton C. Shaw Manufacturing Medal, ASME
2022, International Leader Award, Center for International Affairs, Case Western Reserve University
2022, Distinguished Fellow, International Institute of Acoustics and Vibration (IIAV)
2020, Named one of The 20 Most Influential Professors in Smart Manufacturing, SME
2019, Best Application in Instrumentation and Measurement Award, IEEE
2019, Eli Whitney Productivity Award, SME
2018, Blackall Machine Tool and Gage Award, ASME
2018, Hideo Hanafusa Outstanding Investigator Award, ISFA
2016, Fellow, International Academy for Production Engineering (CIRP)
2014, Distinguished Lecturer, IEEE Instrumentation and Measurement Society
2014, Fellow, Society of Manufacturing Engineers (SME)
2013, Instrumentation and Measurement Society Technical Award, IEEE
2012, Outstanding Associate Editor Award, IEEE Transactions on Instrumentation and Measurement
2011, Research Excellence Award, Department of Mechanical Engineering, University of Connecticut
2010, Member, Connecticut Academy of Science and Engineering
2008, Pratt & Whitney Chair Professorship, University of Connecticut
2008, Distinguished Lecturer, IEEE Electron Devices Society
2008, Fellow, Institute of Electrical and Electronics Engineers (IEEE)
2007, Outstanding Senior Engineering Faculty Award, University of Massachusetts Amherst
2006, Fellow, American Society of Mechanical Engineers (ASME)
1999, Barbara H. and Joseph I. Goldstein Outstanding Junior Engineering Faculty Award, University of Massachusetts Amherst
1996, Early CAREER Award, NSF

Research Interests

Professor Gao's research interests are in the areas of signal transduction mechanisms for multi-physics sensing, mechatronic systems design, stochastic modeling, multi-resolution data analysis, and artificial intelligence/machine learning for improving the observability and control of manufacturing processes and product quality. His research integrates analytical, numerical, and experimental methods, and has led to the inventions of miniaturized sensors, high-speed measurement instruments, and AI-based data analytic methods to enhance in-situ monitoring and control of manufacturing processes (e.g., plastic injection molding, sheet metal stamping, microrolling, etc.) and prognosis of product quality and system performance (e.g., aircraft engines, building HVAC, batteries, etc.). His current research addresses AI-enhanced control, intelligence, and autonomy of hybrid autonomous manufacturing processes (e.g., incremental forming and additive manufacturing), which is part of the recently established NSF Engineering Research Center on Hybrid Autonomous Manufacturing: Moving from Evolution to Revolution (NSF ERC HAMMER). He has published three books, over 400 technical papers (including 200 journal articles), 13 awarded patents, and given more than 120 invited talks.

Other Affiliations

2022 - 2023 , Guest Editor Robotics and Computer-Integrated Manufacturing
2021 - 2022 , Guest Editor Journal of Materials Processing Technology
2020 - PRESENT, Senior Editor IEEE/ASME Transactions on Mechatronics
2019 - 2020 , Guest Editor IEEE/ASME Transactions on Mechatronics
2018 - PRESENT, Editorial Board Member Robotics and Computer Integrated Manufacturing
2018 - PRESENT, Editorial Board Member International Journal of Computer Integrated Manufacturing
2017 - PRESENT, Editorial Board Member Nanomanufacturing and Nanometrology
2016 - 2017 , Guest Editor ASME Journal of Manufacturing Science and Engineering
2009 - 2015 , Associate Editor ASME Journal of Manufacturing Science and Engineering
2008 - 2015 , Associate Editor IFAC Mechatronics, International Federation of Automatic Control
2005 - 2008 , Associate Editor ASME Journal of Dynamic Systems, Measurement, and Controls
2000 - 2008 , Associate Editor IEEE Transactions on Instrumentation and Measurement,

Patents Received

Publications

Cooper, R., Zhang, J., Huang, J., Wolff, S., Cao, J., & Gao, R. X. (2023). Tensile strength prediction in directed energy deposition through physics-informed machine learning and Shapley additive explanations. Journal of Materials Processing Technology, 315
Wang, J., Niu, X., Gao, R. X., Huang, R. X., & Xue, A. X. (2022). Digital twin-driven virtual commissioning of machine tool. Robotics and Computer-Integrated Manufacturing, 81
Guo, Z., Agarwal, M., Cooper, R., Tian, Q., Gao, R. X., Guo, X. X., & Guo, Z. X. (2022). Machine learning for metal additive manufacturing: towards a physics-informed data-driven paradigm. Journal of Manufacturing Systems, 62 , 145-163.
Cooper, R., Zhang, J., Hughes, B., Guo, Z., & Gao, R. X. (2022). Texture-aware ridgelet transform and machine learning for surface roughness prediction. IEEE Transactions on Instrumentation and Measurement, 71
Fan, Z., Hunt, A., & Gao, R. X. (2022). Indirect measurement methods for quality and process control in nanomanufacturing. Nanomanufacturing and Metrology, 5 (3), 209-229.
Zhang, J., Liu, C., & Gao, R. X. (2022). Physics-guided Gaussian Process for HVAC system performance prognosis. Mechanicals Systems and Signal Processing, 179
Zhang, J., Wang, P., & Gao, R. X. (2021). Hybrid machine learning for human action recognition and prediction in assembly. Robotics and Computer-Integrated Manufacturing, 72 , 1-10.
Huang, J., Zhang, J., Chang, Q., & Gao, R. X. (2021). Integrated process-system modeling and control through graph neural network and reinforcement learning. CIRP Annals – Manufacturing Technology, 70 (1), 377-380.
Zhang, J., & Gao, R. X. (2021). Deep Learning-driven data curation and model interpretation for smart manufacturing. Chinese Journal of Mechanical Engineering.
Lin, Y., Wang, J., Huang, R., & Gao, R. X. (2021). Physics-informed meta learning for machining tool wear prediction. Journal of Manufacturing Systems, 62 , 17-27.
Wang, Y., Liu, C., Cooper, R., Wang, X., & Gao, R. X. (2021). Function block-based human-robot collaborative assembly driven by brainwaves. CIRP Annals - Manufacturing Technology, 70 (1).
Arinez, J., Chang, Q., Gao, R. X., Xu, C. X., & Zhang, J. X. (2020). Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook. Journal of Manufacturing Science and Engineering, 142 (11).
Wang, P., Gao, R. X., & Woyczynski, W. X. (2020). Lévy process-based stochastic modeling for machine performance degradation prognosis. IEEE Transactions on Industrial Electronics, 68 (12), 12760-12770.
Zhang, J., Liu, H., Chang, Q., Wang, Y., & Gao, R. X. (2020). Recurrent neural network for motion trajectory prediction in human robot collaborative assembly. CIRP Annals – Manufacturing Technology, 69 (1), 9-12.
Wang, J., Li, Y., Zhao, R., & Gao, R. X. (2020). Physics guided neural network for machining tool wear prediction. Journal of Manufacturing Systems, 57 , 298-310.
Weicherding, J., Gao, R. X., Isaksson, A. X., Landers, R. X., Parisini, T. X., & Yuan, Y. X. (2020). State of AI-Based Monitoring in Smart Manufacturing and Introduction to Focused Section. Mechatronics, IEEE-ASME Transactions on, 25 (5), 2143-2154.
Zhang, F., Yan, J., Fu, P., Wang, J., & Gao, R. X. (2020). Ensemble sparse supervised model for bearing fault diagnosis in smart manufacturing. Robotics and Computer-Integrated Manufacturing, 65
Wang, J., Zhao, R., & Gao, R. X. (2020). Probabilistic Transfer Factor Analysis for Machinery Autonomous Diagnosis Cross Various Operating Conditions. IEEE Transactions on Instrumentation and Measurement, 69 (8), 5335-5344.
Xiong, Q., Zhang, J., Wang, P., Liu, D., & Gao, R. X. (2020). Transferable two-stream convolutional neural network for human action recognition. Journal of Manufacturing Systems, 56 , 605-614.
Shao, S., Yan, R., Lu, Y., Wang, P., & Gao, R. X. (2020). DCNN-based Multi-signal Induction Motor Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 69 (6), 2658-2669.
Sun, C., Chen, X., Yan, R., & Gao, R. X. (2020). Composite-Graph-Based Sparse Subspace Clustering for Machine Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 69 (5), 1850-1859.
Wang, Z., Xu, J., Yan, R., & Gao, R. X. (2020). A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN. IEEE Transactions on Instrumentation and Measurement, 69 (5), 2377-2389.
Fu, P., Wang, J., Zhang, X., Zhang, L., & Gao, R. X. (2020). Dynamic Routing-based Multimodal Neural Network for Multi-sensory Fault Diagnosis of Induction Motor. Journal of Manufacturing Systems, 55 , 264-272.
Grezmak, J., Zhang, J., Wang, P., Loparo, K. A., & Gao, R. X. (2020). Interpretable Convolutional Neural Network through Layer-wise Relevance Propagation for Machine Fault Diagnosis. IEEE Sensors Journal, 20 (6), 3172-3181.
Grezmak, J., Zhang, J., Wang, P., Loparo, K. A., & Gao, R. X. (2020). Interpretable Convolutional Neural Network through Layer-wise Relevance Propagation for Machine Fault Diagnosis. IEEE Sensors Journal, 20 (6), 3172-3181.
Zhao, D., Cheng, W., Gao, R. X., Yan, R. X., & Wang, P. X. (2020). Generalized Vold–Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear. IEEE Transactions on Instrumentation and Measurement, 69 (2), 401-410.
Cooper, C., Zhang, J., Gao, R. X., Wang, P. X., & Ragai, I. X. (2020). Anomaly detection in milling tools using acoustic signals and generative adversarial networks. Procedia Manufacturing, 48 , 372-378.
Cooper, C., Wang, P., Zhang, J., Gao, R. X., Roney, T. X., Ragai, I. X., & Shaffer, D. X. (2020). Convolutional neural network-based tool condition monitoring in vertical milling operations using acoustic signals. Procedia Manufacturing, 49 , 105-111.
Wang, P., & Gao, R. X. (2020). Transfer learning for enhanced machine fault diagnosis in manufacturing. CIRP Annals - Manufacturing Technology, 69 (1), 413-416.
Zhang, J., Wang, P., & Gao, R. X. (2020). Attention Mechanism-Incorporated Deep Learning for AM Part Quality Prediction. Procedia CIRP, 93 , 96-101.
Grezmak, J., Zhang, J., Wang, P., & Gao, R. X. (2020). Multi-stream convolutional neural network-based fault diagnosis for variable frequency drives in sustainable manufacturing systems. Procedia Manufacturing, 43 , 511-518.
Wang, J., Liang, Y., Zheng, Y., Gao, R. X., & Zhang, F. X. (2020). An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples. Renewable Energy, 145 , 642-650.
Gao, R. X., Wang, L. X., Helu, M. X., & Teti, R. X. (2020). Big data analytics for smart factories of the future. CIRP Annals - Manufacturing Technology, 69 (2), 668-692.
Zhang, J., Liu, H., Chang, Q., Wang, L., & Gao, R. X. (2020). Recurrent neural network for motion trajectory prediction in human-robot collaborative assembly. CIRP Annals - Manufacturing Technology, 69 (1), 9-12.
Zhao, D., Wang, T., Gao, R. X., & Chu, F. X. (2019). Signal optimization based generalized demodulation transform for rolling bearing nonstationary fault characteristic extraction. Mechanical Systems and Signal Processing, 134
Wang, J., Fu, P., Zhang, L., Gao, R. X., & Zhao, R. X. (2019). Multilevel Information Fusion for Induction Motor Fault Diagnosis. Mechatronics, IEEE-ASME Transactions on, 24 (5), 2139-2150.
Wang, J., Yan, J., Li, C., Gao, R. X., & Zhao, R. X. (2019). Deep heterogeneous GRU model for predictive analytics in smart manufacturing: Application to tool wear prediction. Computers in Industry, 111 , 1-14.
Wang, J., Ye, L., Gao, R. X., Li, C. X., & Zhang, L. X. (2019). Digital Twin for rotating machinery fault diagnosis in smart manufacturing. International Journal of Production Research, 57 (12), 3920-3934.
Wang, L., Xu, X., Gao, R. X., & Nee, A. X. (2019). Sustainable cybernetic manufacturing. International Journal of Production Research, 57 (12), 3799-3801.
Mo, L., Zeng, L., Liu, S., & Gao, R. X. (2019). Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting. Information, 10 (6).
Cao, P., Fan, Z., Gao, R. X., & Tang, J. X. (2019). Harnessing multi-objective simulated annealing toward configuration optimization within compact space for additive manufacturing. Robotics and Computer-Integrated Manufacturing, 57 , 29-45.
Cao, J., Brinksmeier, E., Fu, M., Gao, R. X., Liang, B. X., Merklein, M. X., Schmidt, M. X., & Yanagimoto, J. X. (2019). Manufacturing of advanced smart tooling for metal forming. CIRP Annals - Manufacturing Technology.
Zhang, J., Wang, P., & Gao, R. X. (2019). Deep learning-based tensile strength prediction in fused deposition modeling. Computers in Industry, 107 , 11-21.
Wang, J., Fu, P., & Gao, R. X. (2019). Machine vision intelligence for product defect inspection based on deep learning and Hough transform. Journal of Manufacturing Systems, 51 , 52-60.
Wang, P., Liu, Z., Gao, R. X., & Guo, Y. X. (2019). Heterogeneous data-driven hybrid machine learning for tool condition prognosis. CIRP Annals - Manufacturing Technology.
Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R. X., & Teti, R. X. (2019). Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Annals - Manufacturing Technology.
Zhang, X., Yin, Z., Gao, J., Liu, J., Gao, R. X., Cao, H. X., & Chen, X. X. (2019). Discrete Time-Delay Optimal Control Method for Experimental Active Chatter Suppression and Its Closed-Loop Stability Analysis. Journal of Manufacturing Science and Engineering, 141 (5).
Wang, A., Gao, R. X., V�ncza, J. X., Kr�ger, J. X., Wang, X. X., Makris, S. X., & Chryssolouris, G. X. (2019). Symbiotic human-robot collaborative assembly. CIRP Annals - Manufacturing Technology, 68 (2), 701-726.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115 , 213-237.
Zhao, D., Cheng, W., Gao, R. X., Yan, R. X., & Wang, P. X. (2019). Generalized Vold-Kalman Filtering for Nonstationary Compound Faults Feature Extraction of Bearing and Gear. IEEE Transactions on Instrumentation and Measurement.
Barry, L., Hatchman, L., Fan, Z., Guralnik, J., Gao, R. X., & Kuchel, G. X. (2019). Reply to: Hands-free but Nonwearable Technology Needed for Outpatient Clinical Gait-speed Assessment: Gait speed in a geriatrics clinic. Journal of the American Geriatrics Society, 67 (1), 184-185.
Shao, S., Yan, R., Lu, Y., Wang, P., & Gao, R. X. (2019). DCNN-based Multi-signal Induction Motor Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement.
Qiao, Q., Wang, J., Ye, L., & Gao, R. X. (2019). Digital Twin for Machining Tool Condition Prediction. Procedia CIRP, 81 , 1388-1393.
Zhang, J., Wang, P., Gao, R. X., Sun, C. X., & Yan, R. X. (2019). Induction Motor Condition Monitoring for Sustainable Manufacturing. Procedia Manufacturing, 33 , 802-809.
Wang, P., & Gao, R. X. (2019). Prognostic Modeling of Performance Degradation in Energy Storage by Lithium-ion Batteries. Procedia Manufacturing, 34 , 911-920.
Sun, C., Chen, X., Yan, R., & Gao, R. X. (2019). Composite Graph-based Sparse Subspace Clustering for Machine Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement.
Grezmak, J., Wang, P., Sun, C., & Gao, R. X. (2019). Explainable Convolutional Neural Network for Gearbox Fault Diagnosis. Procedia CIRP, 80 , 476-481.
Sun, C., Wang, P., Yan, R., Gao, R. X., & Chen, X. X. (2019). Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization. Mechanical Systems and Signal Processing, 114 , 25-34.
Wang, J., Ye, L., Gao, R. X., Li, C. X., & Zhang, L. X. (2018). Digital Twin for rotating machinery fault diagnosis in smart manufacturing. International Journal of Production Research.
Barry, L., Hatchman, L., Fan, Z., Guralnik, J., Gao, R. X., & Kuchel, G. X. (2018). Reply to: Hands-free but Nonwearable Technology Needed for Outpatient Clinical Gait-speed Assessment: Gait speed in a geriatrics clinic. Journal of the American Geriatrics Society.
Zhang, X., He, Y., Gao, R. X., Geng, J. X., Chen, X. X., & Xiang, J. X. (2018). Construction and Application of Multivariable Wavelet Finite Element for Flat Shell Analysis. Acta Mechanica Solida Sinica, 31 (4), 391-404.
Cao, P., Fan, Z., Gao, R. X., & Stangl, J. X. (2018). Design for Additive Manufacturing: Optimization of Piping Network in Compact System With Enhanced Path-Finding Approach. Journal of Manufacturing Science and Engineering, 140 (8).
Zhang, J., Wang, P., Yan, R., & Gao, R. X. (2018). Long short-term memory for machine remaining life prediction. Journal of Manufacturing Systems.
Barry, L., Hatchman, L., Fan, Z., Guralnik, J., Gao, R. X., & Kuchel, G. X. (2018). Design and Validation of a Radio-Frequency Identification-Based Device for Routinely Assessing Gait Speed in a Geriatrics Clinic: Gait Speed in a Geriatrics Clinic. Journal of the American Geriatrics Society.
Wu, D., Jennings, C., Terpenny, J., Kumara, S., & Gao, R. X. (2018). Cloud-Based Parallel Machine Learning for Tool Wear Prediction. Journal of Manufacturing Science and Engineering, 140 (4).
Zhang, J., Wang, P., Gao, R. X., & Yan, R. X. (2018). An Image Processing Approach to Machine Fault Diagnosis Based on Visual Words Representation. Procedia Manufacturing, 19 , 42-49.
Wang, P., Liu, H., Wang, L., & Gao, R. X. (2018). Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. CIRP Annals - Manufacturing Technology, 67 (1), 17-20.
Zhang, J., Wang, P., Yan, R., & Gao, R. X. (2018). Deep Learning for Improved System Remaining Life Prediction. Procedia CIRP, 72 , 1033-1038.
Zhao, D., Li, J., Cheng, W., Wang, P., Gao, R. X., & Yan, R. X. (2018). Vold-Kalman generalized demodulation for multi-faults detection of gear and bearing under variable speeds. Procedia Manufacturing, 26 , 1213-1220.
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. X. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems.
Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. X. (2017). A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests. Journal of Manufacturing Science and Engineering, 139 (7).
Qian, Y., Yan, R., & Gao, R. X. (2017). A multi-time scale approach to remaining useful life prediction in rolling bearing. Mechanical Systems and Signal Processing, 83 , 549-567.
Wu, D., Jennings, C., Terpenny, J., Kumara, S., & Gao, R. X. (2017). Cloud-Based Parallel Machine Learning for Prognostics and Health Management: A Tool Wear Prediction Case Study. Journal of Manufacturing Science and Engineering.
Gordon, G., Kazmer, D., Tang, X., Fan, Z., & Gao, R. X. (2017). Validation of an In-Mold Multivariate Sensor for Measurement of Melt Temperature, Pressure, Velocity, and Viscosity. International Polymer Processing The Journal of the Polymer Processing Society, 32 (4), 406-415.
Wang, P., Gao, R. X., & Yan, R. X. (2017). A deep learning-based approach to material removal rate prediction in polishing. CIRP Annals - Manufacturing Technology.
Wang, J., Zheng, Y., Wang, P., & Gao, R. X. (2017). A virtual sensing based augmented particle filter for tool condition prognosis. Journal of Manufacturing Processes.
Wang, P., Fan, Z., Kazmer, D., & Gao, R. X. (2017). Orthogonal analysis of multi-sensor data fusion for improved quality control. Journal of Manufacturing Science and Engineering.
Wang, P., Ananya, P., Yan, R., & Gao, R. X. (2017). Virtualization and deep recognition for system fault classification. Journal of Manufacturing Systems.
Wu, D., Liu, S., Zhang, L., Terpenny, J., Gao, R. X., Kurfess, T. X., & Guzzo, J. X. (2017). A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. Journal of Manufacturing Systems, 43 , 25-34.
Wang, P., & Gao, R. X. (2017). Automated Performance Tracking for Heat Exchangers in HVAC. IEEE Transactions on Automation Science and Engineering, 14 (2), 634-645.
Gao, R. X., & Wang, P. X. (2017). Through Life Analysis for Machine Tools: From Design to Remanufacture. Procedia CIRP, 59 , 2-7.
Zhang, X., Gao, R. X., Yan, R. X., Chen, X. X., Sun, C. X., & Yang, Z. X. (2016). Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization. Journal of Sound and Vibration [0022460X], 375 , 200-216.
Mendible, G., Kazmer, D., Gao, R. X., & Johnston, S. X. (2016). Estimation of Bulk Melt-Temperature from In-Mold Thermal Sensors for Injection Molding, Part B: Validation. International Polymer Processing The Journal of the Polymer Processing Society [0930777X], 31 (3), 278-284.
Sah, S., Mahayotsanun, N., Peshkin, M., Cao, J., & Gao, R. X. (2016). Pressure and Draw-In Maps for Stamping Process Monitoring. Journal of Manufacturing Science and Engineering [10871357], 138 (9).
Wen, W., Gao, R. X., & Cheng, W. X. (2016). Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation. Shock and Vibration [10709622], 2016 , 13-Jan.
Wang, P., & Gao, R. X. (2016). Markov Nonlinear System Estimation for Engine Performance Tracking. Journal of Engineering for Gas Turbines and Power [07424795], 138 (9).
Wang, P., Gao, R. X., Tang, X. X., & Fan, Z. X. (2016). Sensing Uncertainty Evaluation for Product Quality. Procedia CIRP [22128271], 41 , 706-711.
Zhang, X., Gao, R. X., Yan, R. X., Chen, X. X., Sun, C. X., & Yang, Z. X. (2016). Analysis of Laminated Plates and Shells Using B-Spline Wavelet on Interval Finite Element. International Journal of Structural Stability and Dynamics [02194554].
Zhang, X., Wang, C., Gao, R. X., Yan, R. X., Chen, X. X., & Wang, S. X. (2016). A Novel Hybrid Error Criterion-Based Active Control Method for on-Line Milling Vibration Suppression with Piezoelectric Actuators and Sensors. Sensors [14248220], 16 (1).
Wang, J., Tang, X., Gao, R. X., Duan, L. X., & Zhang, L. X. (2016). On ultrasonic communication through metal structure for machine embedded sensing. Measurement [02632241], 94 , 653-662.
Wang, P., & Gao, R. X. (2016). Stochastic Tool Wear Prediction for Sustainable Manufacturing. Procedia CIRP [22128271], 48 , 236-241.
Wang, P., Gao, R. X., & Fan, Z. X. (2015). Cloud Computing for Cloud Manufacturing: Benefits and Limitations. , 137 (4), -.
Gao, R. X., Wang, Y. X., Teti, R. X., Dornfeld, D. X., Kumara, S. X., Mori, M. X., & Helu, M. X. (2015). Cloud-enabled prognosis for manufacturing. , 64 (2), 749-772.
Nguyen, M., Li, L., Fan, Z., Gao, R. X., Smith, E. X., Ehmann, K. X., & Cao, J. X. (2015). Joining sheet metals by electrically-assisted roll bonding. , 64 (1), 273-276.