Analyses of online monitoring signals for a GMAW process before and after improvement

Abstract

The ability to detect the onset of welding instability is a very powerful tool in welding process monitoring and control. Toward this goal, this study investigates a gas metal arc welding (GMAW) process by analyzing online monitoring signals. Two separate data sets are obtained from the process, which correspond to (a) a stable process after improvement and (b) a relatively unstable process which exhibits spatter and poor weld bead geometry. Voltage, current, wire-feeding speed and line speed signals for both data sets are analyzed and features are extracted from the raw signals using different signal processing techniques. Specifically, phase diagrams, signal distributions, and fast Fourier transform (FFT) methodologies are implemented. The process parameters differ for the data corresponding to the stable and unstable processes rendering the two data sets incomparable. As such, an overlapping region of parameters is selected and this data is used to develop a multi-layer neural network model. The model uses the features extracted to distinguish between the two datasets under the similar input conditions. The trained model is then used to classify data as being from a stable process or an unstable process.