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: 1544595
• 负责人: Chun Zhang
• 金额: $ 21.5万
• 依托单位: Georgia Tech Research Corporation
• 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=1544595&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.

及时准确地识别飞机等机械结构在役中的故障，对于避免灾难的发生有着至关重要的作用。然而，一个主要的挑战是，传感系统依赖于高频信号，在整个大型结构中难以实现高频信号的协调。为了解决这个基本问题，研究小组将利用3D打印技术制造集成传感器结构组件。具体来说，该团队计划创新一种新颖的打印方案，可以将压电换能器（即传感器/执行器耦合元件）嵌入层状复合材料中。由于传感器密集分布在整个结构中，它们的功能就像嵌入结构中的神经系统。这种传感器神经系统与新的控制和指挥系统以及先进的数据和信号处理能力相结合，可以充分发挥最新的计算能力来精确定位故障。利用新兴的增材制造技术生产具有集成、密集分布的主动传感元件的结构系统的新框架，将有可能导致结构自诊断的范式转变。这一进步可能允许在整个结构中获取高质量、主动的审讯数据，然后可用于促进高度准确和可靠的决策。它将导致智力贡献包括：1)开发一种具有机电双场适应性的新传感模式，在整个结构中产生丰富和高质量的数据；2)设计一种增材制造方案，该方案在整个结构中插入压电微型传感器阵列，以实现主动询问；3)制定新的数据分析和逆分析，可以准确识别故障位置/严重程度，指导传感器系统的微调。