Cybernizing Mechanical Structures Through Integrated Sensor-Structure Fabrication

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The timely and accurate detection and identification of faults in various mechanical structures during their operations can play a vitally important role.  Currently, however, there exist major challenges and barriers.  Because the mechanical structures are continuous with infinitely many possible scenarios/patterns of faults that have extremely small characteristic lengths, the sensory system needs to rely on the usage of high-frequency response signals. However, high-frequency interrogation is extremely difficult to realize throughout the entire structure, because of the lack of the capability to truly embed transducers inside a structure.  To tackle the fundamental challenge, the research team seeks a new paradigm building upon the emerging additive manufacturing technology.  Specifically, the team proposes to create novel printing scheme that can embed piezoelectric transducers into layered composites.  As the transducers are densely distributed throughout the entire structure, we can effectively incorporate a nerve system inside the structure.  Such a sensory nerve system, when combined with new sensing modality development and robust and highly accurate data analytics algorithms in cyber space, can fully unleash the latest computing power to pinpoint the fault occurrence.

The proposed new framework of utilizing emerging additive manufacturing technology to produce a structural system inserted with densely distributed active sensing elements will lead to paradigm-shifting progress in structural self-diagnosis.  Through this effort, for the first time we will be able to acquire high-quality active interrogation data throughout the entire structure which will then be used to facilitate highly accurate and robust decision making.  The advancements and scientific merits include: 1) adaptive sensor/actuator array design with wave guiding and circuitry integration to realize directional-guided interrogation; 2) process modeling and optimization of additive manufacturing for structure inserted with active transducer array with enhanced electro-mechanical coupling; and 3) hybrid inverse analysis algorithms featuring physics-informed data analytics for progressive fault identification.  The project has the following broad impacts: 1) development of autonomous structural system with self-diagnosis capability that can benefit aerospace, mechanical, transportation, manufacturing, and other industries; 2) contribution to workforce training by promoting sensing/ manufacturing/ data analytics; and 3) design and analysis methodologies that can be extended to new functional composite material synthesis. 

  • integrated sensor-structure fabrication
  • arrayed sensor-actuator design
  • fault diagnosis
  • process optimization
  • 1544707
  • 1545038
  • 1544595
  • 2018
  • CPS-PI Meeting 2018
  • Poster
  • Posters (Sessions 8 & 11)
Submitted by Jiong Tang on