Abstract
As one of the fundamental building blocks for realizing Industry 4.0, sensors have historically played a critical role in monitoring manufacturing equipment and processes and providing input for product quality control. With the increasing convergence among measurement science, big data analytics, Industrial Internet of Things, and system miniaturization, advanced sensing methods has continually pushed the envelope of smart manufacturing. New sensors not only acquire higher resolution data at faster rates, but also provide local computation for autonomously analyzing the acquired data towards intelligent decision support.

This talk highlights research on multi-physics sensing methods for improved observability in manufacturing process monitoring, using polymer injection molding as an example. The design, characterization, and realization of a multivariate sensor with acoustic-based data transmission capability for data transmission in a RF-shielded environment, together with advanced computational methods, are described. Experiments performed on a production grade injection molding machine demonstrate that a single multivariate sensor can outperform multiple commercial sensors in predicting the quality attributes of injection molded parts. The talk highlights the significance of integrating physics-based sensing with data analytics for advancing the science base and state-of-the-technology to fully realize the potential of smart manufacturing.

Bio
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. Since receiving his Ph.D. degree 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. The goal of his research is to better understand the physical mechanisms underlying the manufacturing processes and equipment performance degradation, to ultimately improve process and 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 was the lead guest editor for the Special Issue on Data Science-Enhanced Manufacturing of the ASME Journal of Manufacturing Science and Engineering, and served as Associate Editor for journals of the IEEE, ASME, and IFAC. He has supervised approximately 40 PhD and MS students to their graduation, holds 12 patents, published two books and over 350 refereed technical papers, including 150 journal articles. He is a recipient of multiple honors and awards, including the SME Eli Whitney Productivity Award, ASME Blackall Machine Tool and Gage Award, ISFA Hideo Hanafusa Outstanding Investigator Award, IEEE Instrumentation and Measurement Society Technical Award, multiple Best Paper awards, Outstanding Faculty Awards, and an NSF CAREER award.