CNN-Based Detection of Welding Crack Defects in Radiographic Non-Destructive Testing
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64362-0_6
Reference28 articles.
1. Kiruthika, M., Bindu, S.: Classification of electrical power system conditions with convolutional neural networks. Eng. Technol. Appl. Sci. Res. 10(3), 5759–5768 (2020). https://doi.org/10.48084/etasr.3512
2. Lavanya, S., Prabakaran, S., Ashok Kumar, N.: A deep learning technique for detecting high impedance faults in medium voltage distribution networks. Eng. Technol. Appl. Sci. Res. 12(6), 9477–9482 (2022)
3. Chakravorty, S.S.H.N.N.J.: ANN and ANFIS for short term load forecasting. Eng. Technol. Appl. Sci. Res. 8(2), 2 (2018)
4. Singh, R.: Welding Processes Handbook. John Wiley & Sons (2023)
5. Chen, Z., Feng, Z., Chen, J.: Key Technologies of Intelligentized Welding Manufacturing isual Sensing of Weld Pool Dynamic Characters and Defect Prediction of GTAW Process. Springer (2021)
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