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Robert Gao, PhD, MS, BS

Cady Staley Professor of Engineering
Department of Mechanical and Aerospace Engineering
Case School of Engineering
Chair
Department of Mechanical and Aerospace Engineering
Case School of Engineering

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.

Research Information

Research Interests

  • 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

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.

 

Education

PhD
Mechanical Engineering
Technical University of Berlin
1991
MS
Mechanical Engineering
Technical University of Berlin
1985
BS
Central Academy of Arts and Design, Beijing, China
1982