不破坏性侵蚀性磨损监测多层涂层使用基于AI启用差分环共振器系统的多层涂层
Vishal Balasubramanian1, Omid Niksan1, Mandeep C Jain1
1Okanagan MicroElectronics and Gigahertz Applications Laboratory, School of Engineering, Faculty of Applied Science, University of British Columbia, Kelowna, BC, V1V 1V7, Canada.
Nature communications
|August 15, 2023
概括
这项研究引入了一种人工智能驱动的微波传感器系统,用于实时,非破坏性检测涂层侵蚀性磨损. 智能系统监测涂层的健康状况,防止航空和海事等关键行业的故障.
科学领域:
- 材料科学 材料科学 材料科学
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 不受保护的表面的侵蚀性磨损导致涂层故障,造成安全隐患和财务损失.
- 航空,海洋和可再生能源等行业需要实时,自主监测涂层完整性.
- 目前用于检测涂层磨损的方法往往不足以进行即时的,自主评估.
研究的目的:
- 开发一个实时的,非破坏性检查系统,用于检测涂料的侵蚀性磨损.
- 利用人工智能 (AI) 和微波传感器进行自主涂层健康监测.
- 提供一种切实可行的解决方案,以防止要求苛刻的工业应用中的涂层故障.
主要方法:
- 利用了支持人工智能的微波差分环共振器传感器,与智能监控电路集成.
- 开发了一种差异微波系统,通过响应器特征的变化来检测涂层侵蚀.
- 使用循环神经网络 (RNN) 进行预测分析,以估计磨损深度和速度.
主要成果:
- 成功验证了系统在单层和多层高分子涂层上的性能,厚度高达2.5毫米.
- 证明了系统在多层涂层中区分侵蚀层的能力.
- 展示了使用集成AI模型精确估计磨损深度和速度.
结论:
- 支持人工智能的微波共振器系统为实时涂层侵蚀监测提供了一个实用的解决方案.
- 这项技术提高了安全性,并通过防止灾难性的涂层故障来减少财务损失.
- 人工智能和智能传感器的协同组合对工业涂层应用非常有效.
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