Abstract
To achieve online testing of high-frequency electric resistance welding (HF-ERW) tube quality, forecasting models were established for welding defect conditions with collected high-speed images of the joint melting phenomenon, based on a radial basis function neural network (RBFNN). Firstly, the dimensions of the collected image samples were deduced by principal component analysis (PCA). Then, the reduced-dimension image samples were set as inputs of both BPNN (back-propagation neural network) and, for comparison, RBFNN, which were trained so that the model parameters were optimized. Finally, the testing sample set was identified by trained networks. The experimental results show that RBFNN had better generalization ability for HF-ERW images than BPNN, which meant that the recognition rate of low-heat input status reached 100%, while the recognition rate of overheating input status reached 97.67%. They also show that the welding quality detection system based on a neural network is very effective and has a strong guiding significance for welding quality control.
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Abbreviations
- a:
-
directional orientation of the system
- h:
-
strip thickness with strip thickness and strip thickness strip thickness
- λ :
-
eigenvalue vector of square matrix
- V:
-
eigenvector matrix of square matrix
- k:
-
the number of eigenvalue of image covariance matrix needed to meet the PCA dimensionality reduction effect
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Wang, HF., Cao, J., Zhao, XM. et al. Detection of HF-ERW status by neural network on imaging. Int. J. Precis. Eng. Manuf. 18, 931–936 (2017). https://doi.org/10.1007/s12541-017-0110-8
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DOI: https://doi.org/10.1007/s12541-017-0110-8