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

CPS/协同/协作研究：通过集成传感器结构制造实现机械结构的网络化

• 批准号: 1545038
• 负责人: Yu Ding
• 金额: $ 20万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545038&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Cybernizing

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.

机械结构中的断层（例如飞机）的及时，准确的服务识别可以在避免灾难中起着至关重要的作用。 但是，一个主要的挑战是，感应系统依赖于高频信号，在大型结构中很难实现其协调。 为了解决这个基本问题，研究团队将利用3D打印技术来制造集成的传感器结构组件。 具体而言，该团队计划创新一种新颖的打印方案，该方案可以将压电传感器（即传感器/执行器耦合元素）嵌入分层复合材料中。 由于传感器在整个结构中密集分布，因此它们像嵌入结构中的神经系统一样起作用。 当与新的控制和命令系统以及高级数据以及信号处理能力结合使用时，这种传感器神经系统可以完全释放最新的计算能力，以查明故障位置。利用新兴的添加剂制造技术的新框架来生产具有集成，密集的，密集的主动感应元素的结构性系统，可以潜在地导致结构性自我diarsississ sississ sispersis sississ。 这种进步可以允许在整个结构中获取高质量的主动审讯数据，然后可以用来促进高度准确且稳健的决策。 这将导致智力贡献，包括：1）开发一种新的感测模式，并具有机械的双场适应性，从而在整个结构中产生丰富而高质量的数据； 2）设计添加剂制造方案，该方案插入整个结构中的压电微型传感器阵列以实现主动询问； 3）可以准确识别故障位置/严重性并指导传感器系统的微调的新数据分析和反向分析的表述。