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Award Abstract #1544595

CPS/Synergy/Collaborative Research: Cybernizing Mechanical Structures through Integrated Sensor-Structure Fabrication

Div Of Civil, Mechanical, & Manufact Inn
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Initial Amendment Date: August 21, 2015
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Latest Amendment Date: August 21, 2015
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Award Number: 1544595
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Award Instrument: Standard Grant
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Program Manager: Bruce Kramer
CMMI Div Of Civil, Mechanical, & Manufact Inn
ENG Directorate For Engineering
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Start Date: January 1, 2016
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End Date: December 31, 2019 (Estimated)
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Awarded Amount to Date: $215,000.00
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Investigator(s): Chun (Chuck) Zhang czhang343@isye.gatech.edu (Principal Investigator)
Ben Wang (Co-Principal Investigator)
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Sponsor: Georgia Tech Research Corporation
Office of Sponsored Programs
Atlanta, GA 30332-0420 (404)894-4819
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Program Reference Code(s): 032E, 033E, 082E, 083E, 7918, 8025, 8235
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Program Element Code(s): 7918


The timely and accurate in-service identification of faults in mechanical structures, such as airplanes, can play a vitally important role in avoiding catastrophes. One major challenge, however, is that the sensing system relies on high frequency signals, the coordination of which is difficult to achieve throughout a large structure. To tackle this fundamental issue, the research team will take advantage of 3D printing technology to fabricate integrated sensor-structure components. Specifically, the team plans to innovate a novel printing scheme that can embed piezoelectric transducers (namely, sensor/actuator coupled elements) into layered composites. As the transducers are densely distributed throughout the entire structure, they function like a nerve system embedded into the structure. Such a sensor nerve system, when combined with new control and command systems and advanced data and signal processing capability, can fully unleash the latest computing power to pinpoint the fault location.

The new framework of utilizing emerging additive manufacturing technology to produce a structural system with integrated, densely distributed active sensing elements will potentially lead to paradigm-shifting progress in structural self-diagnosis. This advancement may allow the acquisition of high-quality, active interrogation data throughout the entire structure, which can then be used to facilitate highly accurate and robust decision-making. It will lead to intellectual contributions including: 1) development of a new sensing modality with mechanical-electrical dual-field adaptivity, that yields rich and high-quality data throughout the structure; 2) design of an additive manufacturing scheme that inserts piezoelectric micro transducer arrays throughout the structure to enable active interrogation; and 3) formulation of new data analytics and inverse analysis that can accurately identify the fault location/severity and guide the fine-tuning of the sensor system.


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Hongyue Sun, Kan Wang, Yifu Li, Chuck Zhang and Ran Jin. "Quality Modeling of Printed Electronics in Aerosol Jet Printing based on Microscopic Images," ASME Transactions, Journal of Manufacturing Science and Engineering, 2017.

Yifu Li, Ran Jin, Hongyue Sun, Xinwei Deng, and Chuck Zhang. "Manufacturing Quality Modeling with Smooth Variable Selection based on Spatial Predictors," 2017 Industrial & Systems Engineering Research Conference (ISERC), 2017.


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