On-Line Monitoring and Prediction of Material Degradation in Mechanical Structures

机械结构中材料退化的在线监测和预测

• 批准号: 9531835
• 负责人: Asok Ray
• 金额: $ 15万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-15 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9531835&HistoricalAwards=false
• 关键词: Line; Monitoring; Prediction; Material; Degradation

9531835 Ray The emphasis of this analytical and experimental research project is to enhance the science and technology base for on- line monitoring of material damage and prediction of residual service life in complex mechanical structures. Potential application areas include automotive and aircraft related mechanical systems, electric power plants, and pulp and paper mills, and discrete production processes such as manufacture of ceramics and semiconductors, where structural durability of plant components and operational reliability are critical. Based on the understanding of physical phenomena and the data available, analytical deterministic models of damage dynamics are formulated for selected materials in a continuous time-setting as opposed to the commonly used cycle-based models of fatigue damage. Stochastic modeling of fatigue dynamics is advanced based on an ensemble of experimentally generated statistical data. An on-line damage monitoring system is built using the stochastic model with series inputs from basic sensing elements. Experimental validation of the concept is accomplished via a test apparatus fabricated in the laboratory with equipment and test samples provided by NASA Lewis and Sikorsky Aircraft Company respectively.***

9531835射线这一分析和实验研究项目的重点是加强复杂机械结构中材料损伤在线监测和剩余使用寿命预测的科学和技术基础。 潜在的应用领域包括汽车和飞机相关的机械系统、发电厂、纸浆和造纸米尔斯厂，以及陶瓷和半导体制造等离散生产过程，其中工厂部件的结构耐久性和操作可靠性至关重要。基于对物理现象和现有数据的理解，损伤动力学的分析确定性模型，制定了选定的材料在一个连续的时间设置，而不是常用的基于周期的疲劳损伤模型。 疲劳动力学的随机建模是先进的基于实验产生的统计数据的合奏。利用该模型建立了一个基于基本传感元件串联输入的损伤在线监测系统。通过实验室制造的试验装置完成了概念的实验验证，该试验装置分别由NASA刘易斯和西科斯基飞机公司提供设备和试验样品。