Awards and Recognitions
Research InterestsProfessor 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.
Areas of Research
Signal Transduction Mechanisms for Smart Manufacturing
- Capacitance-based pressure sensing for microrolling of sheet metals
- Multi-physics sensing for injection molding process monitoring and quality prediction
- Electrical capacitance tomography (ECT) for dynamic process imaging
Physics-Informed AI/Machine Learning for Manufacturing Process Monitoring and Quality Control
- Physics-guided Gaussian process for system performance prognosis
- Texture-aware ridgelet transform for machined surface roughness characterization
- Shapley additive explanations for feature ranking in additive manufacturing
Human Robot Collaboration in Manufacturing
- Temporal and spatial information fusion for human action recognition
- Causal dependency analysis for human action prediction
- Probabilistic recurrent neural network for human trajectory prediction
Stochastic Modeling and Uncertainty Quantification
- Local search particle filter for tool wear degradation prediction
- Markov nonlinear system estimation for engine performance tracking
- Multimodal particle filter for remaining useful life (RUL) prediction
Signal Processing and Multi-Resolution Analysis
- Base wavelet selection for vibration signal analysis
- Harmonic wavelet-based data filter for machine defect identification
- Approximate entropy and complexity measures for machine health evaluation
Mechatronic Systems Design
- Sensor-embedded smart bearing with self-diagnostic capabilities
- Ultrasound sensor integrated long cane for the visually impaired
- Energy efficient wearable electronics for human health management
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, 2006.
R. Gao, P. Wang, and R. Yan, “Machine Tool Prognosis for Precision Manufacturing”, in Precision Manufacturing: Metrology (Ed. W. Gao), Springer, 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.
News About Robert Gao
Robert Gao has a vast international educational background. After growing up in Beijing, China, and completing his undergraduate degree there, he studied in Germany for graduate school and then moved to the United States, where he’s worked at various universities for more than 30 years.
Congratulations to the recipients of a new EMAE Graduate Student Teaching Awards: Clayton Cooper, advisor Prof. Robert Gao Chinmay Shingote, advisor Prof. Chirag Kharangate Pushkal Kannan, advisor Prof. Ya-Ting Liao
Robert Gao named one of the 20 most influential professors in smart manufacturing.
Robert Gao In the News
Mechanical and Aerospace Engineering department chair, Robert Gao, was named one of the 20 most influential professors in smart manufacturing.