发动机涡轮盘裂纹深度定量检测方法

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中图分类号：TB9;TP391 文献标志码：A文章编号：1674-5124（2025）09-0051-06

Abstract： In response to the urgent need for high-precision detection of surface crack depth of curved metal components such as aeroengine turbine discs，a quantitative detection method based on flexible electromagnetic sensors and combining pulse excitation and machine learning was proposed. A diferential flexible electromagnetic sensor and pulse eddy current detection system were designed，and the relationship between multiple time-frequency domain characteristics of the detection signal and crack depth was obtained through simulation analysis.On this basis，an intelligent inversion algorithm for crack depth based onartificial neural networks was proposed. A large amount of simulation data and a small amount of experimental data were applied to the training of inversion model through a combination of data preprocessing and transfer learning，solving the problem of insufficient training samples.The experimental results show that this method can be utilized to detect cracks whose depth ranging from O to 6mm ， and the measurement uncertainty of crack depth is 0.13mm ， which providing method and technical support for quantitative detection of defects of curved metal components.

Keywords： engine turbine disc; crack depth; quantitative detection; flexible sensor; pulsed current

0 引言

涡轮盘等曲面金属构件作为航空发动机的核心部件，由于所受载荷非常复杂，很容易产生疲劳裂纹，需要在定期检修时采用真空等离子焊接等手段进行修复。（剩余8469字）