Department of Mechanical and Aerospace Engineering

ROBERT X. GAO

 


Cady Staley Professor of Engineering
Department Chair, Mechanical & Aerospace Engineering

Contact Information
Office: Glennan 479C
Phone: 216-368-6045

Email: Robert.Gao@case.edu

Research Interest

  • Multi-physics sensing methods, mechatronics
  • Stochastic modeling, multi-resolution data analysis
  • Machine learning, deep learning for advanced manufacturing
  • Cyber physical system condition monitoring, failure root cause diagnosis, and remaining useful life (RUL) prognosis
  • RF and acoustic-based wireless data transmission

Education

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

Select Recognition

  • IEEE Best Application in Instrumentation and Measurement Award, 2019.
  • SME Eli Whitney Productivity Award, 2019.
  • ASME Blackall Machine Tool and Gage Award, 2018
  • ISFA Hideo Hanafusa Outstanding Investigator Award, 2018
  • Distinguished Lecturer:
    -IEEE Instrumentation and Measurement Society, 2014-2017
    -IEEE Electron Devices Society, 2008-2013
  • Fellow:
    -The International Academy for Production Engineering (CIRP), 2016
    -Society of Manufacturing Engineers (SME), 2014
    -Institute of Electrical and Electronics Engineers (IEEE), 2008
    -American Society of Mechanical Engineers (ASME), 2006
  • Member: Connecticut Academy of Science and Engineering, 2010
  • IEEE Technical Award, IEEE Instrumentation and Measurement Society, 2013
  • Outstanding Associate Editor Award: IEEE Transactions on Instrumentation and Measurement, 2012
  • Multiple Best Paper/Best Student Paper Awards
  • Pratt & Whitney Chair Professorship, University of Connecticut, 2008-2015
  • Research Excellence Award, Department of Mechanical Engineering, University of Connecticut, 2011
  • Outstanding Senior Faculty Award, University of Massachusetts Amherst, 2007
  • Barbara H. and Joseph I. Goldstein Outstanding Junior Engineering Faculty Award, University of Massachusetts Amherst, 1999
  • NSF Early CAREER Award, 1996

Select Professional Service

  • Guest Editor:
    -ASME Journal of Manufacturing Science and Engineering, Special Issue on Data Science-   Enhanced Manufacturing, 2016-2017;
    -Mathematical Problems in Engineering, Special Issue on Cyber Physical Systems, 2015.
  • Associate Editor:
    -ASME Journal of Manufacturing Science and Engineering, 2009 – 2015;
    -Mechatronics, International Federation of Automatic Control, 2008 – 2015;
    -IEEE Transactions of Instrumentation and Measurement, 2000-2008, and 2010 – 2013;
    -ASME Journal of Dynamic Systems, Measurement, and Controls, 2005-2008.
  • Editorial Board Member:
    -Robotics and Computer Integrated Manufacturing, 2018 – present;
    -International Journal of Computer Integrated Manufacturing, 2018 – present;
    -Nanomanufacturing and Nanometrology, 2017 – present;
    -Smart and Sustainable Manufacturing Systems, 2016 – present;
    -International Journal of Manufacturing Research, 2006 – present.

Select Publications

Books and Book Chapters

  • R. Gao and R. Yan, “Wavelet: Theory and Application for Manufacturing”, English Edition, Springer, New York, Dordrecht, Heidelberg, London, ISBN 978-1-4419-1544-3, 2011; Chinese Edition, Machinery Industry Press, ISBN 978-7-111-61407-4, 2019.
  • L. Wang and R. Gao (Eds.), “Condition Monitoring and Control for Intelligent Manufacturing”, Springer, UK, ISBN 1-84628-268-3, May, 2006.
  • R. Gao, P. Wang, and R. Yan, “Machine Tool Prognosis for Precision Manufacturing”, in Precision Manufacturing: Metrology (ed. W. Gao), Springer, in press, 2018.
  • R. Gao and P. Wang, “Sensors to Control Processing and Improve Lifetime and Performance for Sustainable Manufacturing”, in Encyclopedia of Sustainable Technologies, Elsevier, (Ed. M. Abraham), pp. 447-462, DOI: 10.1016/B978-0-12-409548-9.10217-9, May, 2017.
  • S. Liu and R. Gao, “Multisensor Data Fusion: Architecture Design and Application in Physical Activity Assessment”, in Multisensor Data Fusion: From Algorithm and Architecture design to Applications (Eds. H. Fourati and K. Iniewski), CRC Press, March, 2015.
  • Z. Fan, R. Gao, and J. Wang, “Virtual Instrumentation for Electrical Capacitance Tomography”, in LabView: Practical Applications and Solutions, InTech, ISBN 978-953-307-650-8, 2011.

Recent Journal Articles

  • J. Wang, P. Fu, and R. Gao, “Machine vision intelligence for product defect inspection based on deep learning and Hough transform”, Journal of Manufacturing Systems, Vol. 51, pp. 52-60, 2019.
  • X. Zhang, H. Zhang, J. Gao, J. Liu, R. Gao, H. Cao, and X. Chen, “Discrete time-delay optimal control method for experimental active chatter suppression and its closed-loop stability analysis”, ASME Journal of Manufacturing Science and Engineering, Vol. 141, pp. 051003-1-13, 2019.
  • R. Zhao, R. Yan, P. Wang, and R. Gao, “Deep learning and its applications to machine health monitoring”, Mechanical Systems and Signal Processing, vol. 115, pp. 213-237, 2019.
  • P. Cao, Z. Fan, R. Gao, and J. Tang, “Harnessing multi-objective simulated annealing toward configuration optimization within compact space for additive manufacturing”, Robotics and Computer-Integrated Manufacturing, Vol. 57, pp. 29-45, 2019.
  • J. Wang, R. Gao, Z. Yuan, Z. Fan, and L. Zhang, “A joint particle filter and expectation maximization approach to machine condition prognosis”, Journal of Intelligent Manufacturing, Vol. 30, No. 2, pp. 605–621, 2019.
  • C. Sun, P. Wang, R. Yan, R. Gao, and X. Chen, “Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization”, Mechanical Systems and Signal Processing, Vol. 114, pp. 25-34, 2018.
  • J. Zhang, P. Wang, R. Yan, and R. Gao, “Long short-term memory for machine remaining life prediction”, Journal of Manufacturing Systems, Vol. 48, pp. 78-86, 2018.
  • P. Wang, H. Liu, L. Wang, and R. Gao, “Deep learning-based human motion recognition for predictive context-aware human-robot collaboration”, CIRP Annals-Manufacturing Technology, Vol. 67, No. 1, pp. 17-20, 2018.
  • X. Zhang, Y. He, R. Gao, J. Geng, X. Chen, and J. Xiang, “Construction and application of multivariable wavelet finite element for flat shell analysis”, Acta Mechanica Solida Sinica, Vol. 31, No. 4, pp. 391-404, 2018.
  • P. Cao, Z. Fan, R. Gao, and J. Tang, “Design for additive manufacturing: optimization of piping network in compact system with enhanced path-finding approach”, ASME Journal of Manufacturing Science and Engineering, Vol. 140, 0810113-1-15, August, 2018.
  • D. Wu, C. Jennings, J. Terpenny, S. Kumara, and R. Gao, “Cloud-based parallel machine learning for tool wear prediction”, ASME Journal of Manufacturing Science and Engineering, Vol. 140, 041005-1-10, April, 2018.
  • J. Wang, Y. Ma, L. Zhang, R. Gao, and D. Wu, “Deep learning for smart manufacturing: methods and applications”, Journal of Manufacturing Systems, Vol. 48, Part C, pp. 1444-156, 2018.
  • L. Barry, L. Hatchman, Z. Fan, J. Guralnik, R. Gao, and G. Kuchel, “Design and validation of a RFID-based device for routinely assessing gait speed in a geriatrics clinic”, Journal of the American Geriatrics Society, pp. 8614-8618, January, 2018.
  • P. Wang, Z. Fan, D. Kazmer, and R. Gao, “Orthogonal analysis of multi-sensor data fusion for improved quality control”, ASME Journal of Manufacturing Science and Engineering, Vol. 139, 101008-1–8, 2017.
  • P. Wang, R. Gao, and R. Yan, “A deep learning-based approach to material removal rate prediction in polishing”, CIRP Annals-Manufacturing Systems, Vol. 66, No. 1, pp. 429-432, 2017.
  • P. Wang, Ananya, R. Yan, and R. Gao, “Visualization and deep recognition for system fault classification”, Journal of Manufacturing Systems, Vol. 44, pp. 310-316, 2017.
  • J. Wang, Y. Zheng, P. Wang, and R. Gao, “A virtual sensing based augmented particle filtering for tool condition prognosis”, Journal of Manufacturing Processes, Vol. 28, pp. 472-478, 2017.
  • P. Wang and R. Gao, “Automated performance tracking for heat exchangers in HVAC”, IEEE Transactions on Automation Science and Engineering, Vol. 14, No. 2, pp. 634-645, 2017.
  • J. Wang, L. Zhang, L. Duan, and R. Gao, “A new paradigm of cloud-based predictive maintenance for intelligent manufacturing”, Journal of Intelligent Manufacturing, Vol. 28, No. 5, pp. 1125-1137, 2017.
  • G. Gordon, D. Kazmer, X. Tang, Z. Fan, and R. Gao, “Validation of an in-mold multivariate sensor for measurement of melt temperature, pressure, velocity, and viscosity”, International Polymer Processing, Vol. 32, No. 4, pp. 406-415, 2017.
  • D. Wu, C. Jennings, J. Terpenny, R. Gao, and S. Kumara, “A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forest”, ASME Journal of Manufacturing Science and Engineering, Vol. 139, No. 7, pp. 0710-0718, 2017.
  • D. Wu, S. Liu, L. Zhang, J. Terpenny, R. Gao, T. Kurfess, and J. Guzzo, “A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing”, ASME Journal of Manufacturing Science and Engineering, Vol. 43, pp. 25-34, 2017.
  • Y. Qian, R. Yan, and R. Gao, “A multi-time scale approach to remaining useful life prediction in rolling bearing”, Mechanical Systems and Signal Processing, Vol. 83, pp. 549-567, 2017.
  • X. Zhang, R. Gao, R. Yan, X. Chen, C. Sun, and Z. Yang, “Analysis of laminated plates and shells using B-Spline wavelet on interval finite element”, International Journal of Structural Stability and Dynamics, Vol. 17, No. 4, pp. 1750062-1-18, August, 2016.
  • P. Wang and R. Gao, “Markov nonlinear system estimation for engine performance tracking”, ASME Journal Engineering for Gas Turbine and Power, Vol. 138, No. 9, pp. 091201, 2016.
  • G. Mendible, D. Kazmer, R. Gao, and S. Johnston, “Estimation of bulk melt-temperature from in-mold thermal sensors for injection molding, Part B: Validation”, International Polymer Processing, Vol. 31, No. 3, pp. 278-284, 2016.
  • X. Zhang, R. Gao, R. Yan, X. Chen, C. Sun and Z. Yang, “Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization”, Journal of Sound and Vibration, Vol. 375, No. 4, pp. 200-216, 2016.
  • S. Sah, N. Mahayotsanun, M. Peshkin, J. Cao, and R. Gao, “Pressure and draw-in maps for stamping process monitoring”, ASME Journal of Manufacturing Science and Engineering, Vol. 138, No. 9, 2016.
  • X. Zhang, C. Wang, R. Gao, R. Yan, X. Chen and S. Wang, ”A novel hybrid error criterion-based active control method for on-line milling vibration suppression with piezoelectric actuators and sensors”, Sensors, Vol. 16, Issue 1, paper #68, 2016.
  • X. Zhang, R. Gao, R. Yan, X. Chen, C. Sun and Z. Yang, “B-spline wavelet on interval finite element method for static and vibration analysis of stiffened flexible thin plate”, Computers, Materials & Continua, Vol. 52, No. 1, pp. 53-71, 2016.
  • P. Wang, R. Gao, and Z. Fan, “Cloud Computing for Manufacturing: Benefits and Limitations”, ASME Journal of Manufacturing Science and Engineering, Vol. 137, No. 4, pp. 040901, 2015.
  • P. Wang, D. Karg, R. Gao, Z. Fan, K. Kwolek, and A. Consiglio, “Non-Contact Identification of Rotating Blade Vibration”, JSME Mechanical Engineering Journal, Vol. 2, No. 3, pp. 1-12, 2015.
  • P. Wang and R. Gao, “Adaptive Resampling-Based Particle Filtering For Tool Life Prediction”, Journal of Manufacturing Systems, Vol. 37, No. 2, pp. 528-534, 2015.
  • J. Wang, P. Wang, and R. Gao, “Enhanced Particle Filter for Tool Wear Prediction”, Journal of Manufacturing Systems, Vol. 36, pp. 35-45, 2015.
  • S. Johnston, G. Mendible, R. X. Gao, and D. Kazmer, “Estimation of bulk melt-temperature from in-mold thermal sensors for injection molding, Part A: Method”, International Polymer Processing, Vol. 30, No. 4, pp. 460-466, 2015.
  • P. Wang and R. Gao, “Adaptive resampling-based particle filtering for tool life prediction”, Journal of Manufacturing Process, Vol. 37, No. 2, pp. 528-534, 2015. 
  • R. Yan, R. Gao and L. Zhang, “In-process modal parameter identification for spindle health monitoring”, Mechatronics, Vol. 31, pp. 42-49, 2015.
  • R. Gao, L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu, “Cloud-enabled prognosis for manufacturing”, CIRP Annals – Manufacturing Technology, Vol. 64 No. 2, pp. 749-772, 2015.
  • P. Wang, R. Gao, and Z. Fan, “Cloud computing for cloud manufacturing: benefits and limitations”, ASME Journal of Manufacturing Science and Engineering, Vol. 137, 040901-1-9, 2015.
  • G. Gordon, D. Kazmer, X. Tang, Z. Fan, and R. Gao, “In-mold multivariate sensing of colored polystyrene”, Polymer Engineering and Science, Vol 55, No. 12, pp. 2794-2800, 2015.
  • J. Wang, P. Wang, and R. Gao, “Enhanced particle filter for tool wear prediction”, Journal of Manufacturing Systems, Vol. 36, pp. 35-45, 2015.
  • P. Wang, D. Karg, Z. Fan, R. Gao, K. Kwolek, and A. Consiglio, “Non-contact identification of rotating blade vibration”, JSME Mechanical Engineering Journal, Japan Society of Mechanical Engineering, Vol. 2, No. 3, pp. 1-12, 2015.
  • M. Ng, L. Li, Z. Fan, R. Gao, E. Smith, K. Ehmann, and J. Cao, “Joining sheet metals by electrically-assisted roll bonding”, CIRP Annals – Manufacturing Technology, Vol. 64, pp. 273-276, 2015.
  • Z. Fan, X. Zou, R. Gao, M. Ng, J. Cao, and E. Smith, “Embedded capacitive pressure sensing for electrically assisted microrolling”, IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 3, pp. 1005-1014, 2015.
  • D. Kazmer, G. Gordon, G. Mendible, S. Johnston, X. Tang, Z. Fan, and R. Gao, “A multivariate sensor for intelligent polymer processing”, IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 3, pp. 1015-1023, 2015.
  • G. Gordon, D. Kazmer, X. Tang, Z. Fan, and R. Gao, “Quality control using a multivariate injection molding sensor”, International Journal of Advanced Manufacturing Technology, Vol. 78, pp. 1381-1391, June, 2015.
  • X. Zou, Z. Fan, R. Gao, and J. Cao, “An integrative approach to spatial mapping of pressure distribution in microrolling”, CIRP Journal of Manufacturing Science and Technology, Vol. 9, pp. 107-115, 2015.