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.

及时、准确地识别飞机等机械结构的故障，对于避免灾难的发生具有重要的意义。 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。 为了解决这个根本问题，研究团队将利用3D打印技术来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型印刷方案，可以将压电换能器（即传感器/致动器耦合元件）嵌入分层复合材料中。 由于换能器密集地分布在整个结构中，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，可以充分释放最新的计算能力来确定故障位置。利用新兴的增材制造技术来生产具有集成的、密集分布的主动传感元件的结构系统的新框架将可能导致结构自诊断的范式转变进展。 这种进步可以允许在整个结构中获取高质量的主动询问数据，然后可以使用这些数据来促进高度准确和鲁棒的决策。 它将导致智力贡献，包括：1）开发具有机电双场自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）设计增材制造方案，在整个结构中插入压电微换能器阵列，以实现主动询问;以及3）能够准确地识别故障位置/严重性并指导传感器系统的微调的新数据分析和逆分析的公式化。