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Analysis Tool and Methodology Design for Electronic Vibration Stress Understanding and Prediction

机译:电子振动应力理解与预测的分析工具和方法论设计

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The objectives of this research were to (1) understand the impact of vibration on electronic components under ultrasound excitation; (2) model the thermal profile presented under vibration stress; and (3) predict stress level given a thermal profile of an electronic component. Research tasks included: (1) retrofit of current ultrasonic/infrared nondestructive testing system with sensory devices for temperature readings; (2) design of software tool to process images acquired from the ultrasonic/infrared system; (3) developing hypotheses and conducting experiments; and (4) modeling and evaluation of electronic vibration stress levels using a neural network model. Results suggest that (1) an ultrasonic/infrared system can be used to mimic short burst high vibration loads for electronics components; (2) temperature readings for electronic components under vibration stress are consistent and repeatable; (3) as stress load and excitation time increase, temperature differences also increase; (4) components that are subjected to a relatively high pre-stress load, followed by a normal operating load, have a higher heating rate and lower cooling rate. These findings are based on grayscale changes in images captured during experimentation. Discriminating variables and a neural network model were designed to predict stress levels given temperature and/or grayscale readings. Preliminary results suggest a 15.3% error when using grayscale change rate and 12.8% error when using average heating rate within the neural network model. Data were obtained from a high stress point (the corner) of the chip.
机译:这项研究的目的是(1)了解振动对超声激发下电子元件的影响; (2)对振动应力下的热分布进行建模; (3)在给定电子部件的热特性的情况下预测应力水平。研究任务包括:(1)对当前的超声波/红外无损检测系统进行改造,并配备用于温度读数的传感装置; (2)处理从超声/红外系统获取的图像的软件工具的设计; (3)提出假设并进行实验; (4)使用神经网络模型对电子振动应力水平进行建模和评估。结果表明:(1)超声/红外系统可用于模拟电子元件的短脉冲高振动载荷; (2)振动应力下电子元件的温度读数一致且可重复; (3)随着应力负荷和激励时间的增加,温差也增加; (4)承受相对较高的预应力负载,然后再承受正常运行负载的组件,具有较高的加热速率和较低的冷却速率。这些发现基于实验期间捕获的图像的灰度变化。区分变量和神经网络模型被设计为在给定温度和/或灰度读数的情况下预测应力水平。初步结果表明,使用神经网络模型内的灰度变化率时,误差为15.3%;使用平均加热率时,误差为12.8%。从芯片的高应力点(拐角)获得数据。

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