Performances of dimension reduction techniques for welding quality prediction based on the dynamic resistance signal

In this investigation, the welding quality and the dynamic resistance signal in the welding process were correlated using different dimension reduction techniques and regression models. The 0.4-mm-thickness TC2 titanium alloy was used as the welding material, while the welding experiments were carried out by a small pneumatic high-frequency alternating current welding machine. The Rogowski coil and alligator type wire clips were respectively utilized to collect the welding current and voltage in the welding process. The mechanism of dynamic resistance signal changes was studied and explained. In order to extract the features related to the welding quality and reveal the efficiency of the automatic feature extraction method based on the dimension reduction techniques, several dynamic resistance signals with different welding process parameters were processed by the approaches of principal component analysis (PCA), isometric mapping (Isomap), and locally linear embedding (LLE). After that, the redundant information in the dynamic resistance signal which has little to do with the welding quality would be removed and the useful features could also be isolated. And then three regression models quantifying the extracted features and the nugget size were created and their performances were also compared with each other. The results implied that the regression model based on the LLE technique was more robust than those established on the basis of the features automatically extracted from the dynamic resistance signal using PCA and Isomap. Compared with the existing popular manual feature extraction methods in the previous research work, the method proposed in this investigation outperforms the other methods in terms of two aspects. It not only can minimize the prior assumptions about the certain shape of the dynamic resistance curve and remove the subjective factors caused by the manual extraction method, but can assess and monitor the welding quality with a good level of reliability.