EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM

Abstract

A portable spectrometer based on a linear CCD is designed with real-time acquisition and processing of spectral data in the welding process of aluminum alloys. An innovative method is introduced to diagnose and detect porosity defects. The method extracts several characteristic spectral lines and calculates the intensity ratio between H I and Ar I. The intensity ratio is used to diagnose extraordinary cases of hydrogen content. Empirical mode decomposition (EMD) is used to acquire adaptive decomposition of the ratio signal, which has been proved to have better performance in eliminating the influence of pulse current on the ratio signal than wavelet packet transform. Experiments based on X-ray inspection are designed to verify the proposed method. Monitoring of the arc atmosphere and detection of porosity under different welding processes is achieved by extracting the feature parameters. An improved support vector machine (SVM) classification model based on a genetic algorithm (GA) is built in order to guarantee accurate estimation of different types of porosity defects.