Welding anomaly detection based on supervised learning and unsupervised learning

In order to solve the problem of automatic defect detection and process control in the welding and arc additive process, the paper monitors the current, voltage, audio, and other data during the welding process and extracts the minimum value, standard deviation, deviation from the voltage and current data. It extracts spectral features such as root mean square, spectral centroid, and zero-crossing rate from audio data, fuses the features extracted from multiple sensor signals, and establishes multiple machine learning supervised and unsupervised models. They are used to detect abnormalities in the welding process. The experimental results show that the established multiple machine learning models have high accuracy, among which the supervised learning model, the balanced accuracy of Ada boost is 0.957, and the unsupervised learning model Isolation Forest has a balanced accuracy of 0.909.