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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:本研究的目标是(1)了解超声激励下振动对电子元件的影响; (2)模型在振动应力下呈现的热调; (3)预测电子元件的热曲线的压力水平。研究任务包括:(1)电流超声波/红外非破坏性测试系统具有用于温度读数的感官装置; (2)软件工具的设计,以处理从超声波/红外系统获取的图像; (3)开发假设和进行实验; (4)使用神经网络模型建模和评估电子振动应力水平。结果表明(1)超声波/红外系统可用于模仿电子部件的短脉冲高振动载荷; (2)振动应力下的电子元件的温度读数是一致和可重复的; (3)作为压力负荷和激发时间增加,温度差异也增加; (4)经受相对高的预应力负荷的组分,其次是正常工作负荷,具有更高的加热速率和较低的冷却速率。这些发现基于在实验期间捕获的图像的灰度变化。鉴别变量和神经网络模型被设计为预测给定温度和/或灰度读数的应力水平。初步结果建议使用灰度变化率和在神经网络模型中的平均加热速率时出现12.8%的错误时出错。数据是从芯片的高应力点(角落)获得的。

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