Robert X. Gao, Ph.D.

Cady Staley Professor of Engineering and Chair, Department of Mechanical and Aerospace Engineering, Case Western Reserve University, USA


The exponential growth of data has provided new opportunities for the manufacturing community to leverage data science and advance the state of manufacturing. This necessitates research into advanced data analytics to help manufacturers more effectively and efficiently identify patterns and context hidden in raw data sets to gain a deeper understanding of the process physics and derive new knowledge for process and quality control. Beyond individual machines and processes, actionable information generated by data science can further improve the system-level operations of manufacturing enterprises by increasing the accuracy and reliability in predicting equipment failure rates and remaining useful life for preventative maintenance, and improving decision-making through automated, data-driven decision evaluation.

This presentation highlights recent advancement in machine learning as an enabling tool for complex systems modeling, using material removal rate (MRR) prediction in chemical mechanical polishing of semi-conductor wafers and human robot collaboration (HRC) as examples.  Complexity involved in chemical mechanical polishing makes it challenging to accurately predict MRR based solely on physical models.  Deep Belief Network (DBN), which is a representative Deep Learning techniques, has been investigated to reveal the relationship between MRR and polishing operation parameters. Comparison with results from physical models has confirmed the performance of the data-driven modeling approach. As robots are increasingly deployed in manufacturing, improving the efficiency and safety of human robot collaboration has gained increasing attention. Images of human operator’s motion provide informative clues about the specific task to be performed, thus can be explored for establishing accurate and reliable context awareness. Towards this goal, Deep Convolutional Neural Network (DCNN) has been studied for continuous human motion analysis and future HRC needs prediction to improve robot planning and control.  These case studies demonstrate the effectiveness of machine learning for a wide range of engineering applications, where increased computational intelligence will continue to complement and drive the advancement of manufacturing science towards smart factories of the future.


Robert Gao is the Cady Staley Professor of Engineering and Department Chair of Mechanical and Aerospace Engineering at Case Western Reserve University in Cleveland, Ohio, USA.  He was previously the Pratt & Whitney Chair Professor of Mechanical Engineering at the University of Connecticut. Since receiving his Ph.D. from the Technical University of Berlin, Germany in 1991, he has been working on physics-based sensing methods, design, modeling, and characterization of instrument systems, stochastic modeling and machine learning techniques for improving the observability of dynamical systems such as manufacturing equipment and processes, towards for improved product quality control.

Prof. Gao is a Fellow of the ASME, IEEE, SME, and CIRP (International Academy for Production Engineering), and an elected member of the Connecticut Academy of Science and Engineering. He was a Distinguished Lecturer of the IEEE Instrumentation and Measurement Society and Electron Devices Society.  He served as a Guest Editor for the Special Issue on Data Science-Enhanced Manufacturing of the ASME Journal of Manufacturing Science and Engineering, and was Associate Editor for journals of the IEEE, ASME, and IFAC. He has graduated over 40 PhD and MS students, holds 10 patents, published two books, over 140 journal articles and 220 conference proceedings papers. He is a recipient of the ASME Blackall Machine Tool and Gage Award, IEEE Instrumentation and Measurement Society’s Technical Award and Outstanding Associate Editor Award, ISFA Hideo Hanafusa Outstanding Investigator Award, multiple Best Paper and Best Student Paper awards, Outstanding Faculty Awards, and an NSF CAREER award.