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Intelligent Life Prediction of Thermal Barrier Coating for Aero Engine Blades

机译：航空发动机刀片热阻涂层智能寿命预测

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摘要

The existing methods for thermal barrier coating (TBC) life prediction rely mainly on experience and formula derivation and are inefficient and inaccurate. By introducing deep learning into TBC life analyses, a convolutional neural network (CNN) is used to extract the TBC interface morphology and analyze its life information, which can achieve a high-efficiency accurate judgment of the TBC life. In this thesis, an Adap–Alex algorithm is proposed to overcome the problems related to the large training time, over-fitting, and low accuracy in the existing CNN training of TBC images with complex tissue morphologies. The method adjusts the receptive field size, stride length, and other parameter settings and combines training epochs with a sigmoid function to realize adaptive pooling. TBC data are obtained by thermal vibration experiments, a TBC dataset is constructed, and then the Adap–Alex algorithm is used to analyze the generated TBC dataset. The average training time of the Adap–Alex method is significantly smaller than those of VGG-Net and Alex-Net by 125 and 685 s, respectively. For a fixed number of thermal vibrations, the test accuracy of the Adap–Alex algorithm is higher than those of Alex-Net and VGG-Net, which facilitates the TBC identification. When the number of thermal vibrations is 300, the accuracy reaches 93%, and the performance is highest.
机译：现有的热障涂层（TBC）寿命预测方法主要依赖于经验和公式推导，效率低下和不准确。通过向TBC生命分析引入深度学习，卷积神经网络（CNN）用于提取TBC接口形态并分析其生命信息，可以实现对TBC寿命的高效率准确判断。在本文中，提出了一种ADAP-ALEX算法，以克服与复杂组织形态的TBC图像现有CNN训练中的大型训练时间，过度拟合和低精度相关的问题。该方法调整接收场大小，步幅长度和其他参数设置，并将训练时期与SIGMOID功能相结合以实现自适应汇集。 TBC数据通过热振动实验获得，构建了TBC数据集，然后使用ADAP-ALEX算法来分析生成的TBC数据集。 ADAP-ALEX方法的平均培训时间分别小于VGG-NET和ALEX-NET的培训时间，分别为125和685秒。对于固定数量的热振动，ADAP-ALEX算法的测试精度高于Alex-Net和VGG-Net的测试精度，这有利于TBC识别。当热振动的数量为300时，精度达到93％，性能最高。

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