Fatigue Damage Detection of Composite Materials Based on Fiber Optic Vibration Sensing and Neural Network Prediction Techniques

基于光纤振动传感和神经网络预测技术的复合材料疲劳损伤检测

• 批准号: 9714045
• 负责人: Melek Yalcintas
• 金额: $ 1.8万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 1999-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9714045&HistoricalAwards=false
• 关键词: Fatigue; Damage; Detection; Composite; Materials

9714045 Yalcintas This Research Planning Grant (RPG) facilitates preliminary studies related to the development of a research proposal on fatigue damage detection of composite materials. The technique is based on the change of vibration characteristics of composites as they experience fatigue damage. Fiber optic sensors are used to measure the vibration response of the composite structures. Neural network prediction techniques are then employed to evaluate the measured vibration information, and predict the health conditions and damage state of the composite materials. The project addresses the limitations of composites for defense, space, automotive, and aerospace industries on account of their brittle nature and low fracture toughness. A continuous fatigue damage detection method is developed that can inform the user of the fatigue stage and remaining life of composite materials. ***

9714045 Yalcintas这项研究计划补助金（RPG）促进有关的研究提案的发展对复合材料的疲劳损伤检测的初步研究。该技术是基于复合材料的振动特性的变化，因为他们经历疲劳损伤。光纤传感器用于测量复合材料结构的振动响应。然后，神经网络预测技术，评估测量的振动信息，并预测的健康状况和复合材料的损伤状态。该项目解决了国防，空间，汽车和航空航天工业复合材料的限制，因为它们的脆性和低断裂韧性。提出了一种连续疲劳损伤检测方法，能够及时地给出复合材料的疲劳状态和剩余寿命。 ***