Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH

In double pulse metal inert gas (DPMIG) welding, the input broadband electrical signals are often affected by strong noise, which will decrease the quality monitoring accuracy. Therefore, a suitable method should be applied to extract features from the signals. However, due to the Gibbs phenomenon and the interpolation of extreme points, former methods such as variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) will generate unavoidable error. Therefore, broadband mode decomposition (BMD) method is newly proposed in this paper by constructing an associative dictionary library consisting of typical broadband and narrowband signals. Therefore, the drawbacks of the former methods can be avoided by searching in the dictionary. Analysis results indicate that by combining with flexible convex hulls (MMC-FCH), BMD is more accurate in extracting broadband components. Meanwhile, the mean accuracy of quality monitoring can be increased from 92.19% (VMD) and 93.75% (EEMD) to 100% by applying BMD. (C) 2020 Elsevier Ltd. All rights reserved.