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W2891526384 abstract "Based on the ultrasonic and Barkhausen non-destructive testing methods, the feature extraction and recognition capabilities of deep learning methods were obtained to quantitatively characterize the surface hardness characteristics of typical materials. Firstly, the hardness value of 45 steel after different heat treatment was measured by Brinell hardness tester, and then the speed characteristics of ultrasonic propagation in 45 steel after different heat treatment and the Barkhausen noise signal in different steel specimens were measured through experiments. A polynomial fitting method was used to calibrate the mapping curve and verify its detection accuracy. The deep learning method was used to realize the multi-dimensional comprehensive characterization of ultrasonic steel speed and Barkhausen noise signal to 45 steel hardness value, and the feasibility and accuracy advantages of the joint detection method were verified." @default.
W2891526384 created "2018-09-27" @default.
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W2891526384 date "2018-01-01" @default.
W2891526384 modified "2023-09-25" @default.
W2891526384 title "Research on Joint Nondestructive Testing Based on Neural Network" @default.
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W2891526384 doi "https://doi.org/10.1007/978-981-13-2384-3_43" @default.
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