Aluminum alloy oxidation prediction during laser welding process based on random forest regression analysis of spectral signals

Abstract

Aluminum alloys are one of the most important materials in modern industries; however, they are susceptible to oxidation during the welding process. In an automated welding process, the online monitoring and prediction of weld bead oxidation degree are particularly important. This study proposes a novel method to real-timely predict the oxidation degree of the aluminum alloy during the laser welding process based on the laser plasma spectral signals. First, the characteristics of laser plasma spectral signals are analyzed under various oxidation degree conditions. And then, a random forest regression model is built to extract the principal characteristic wavelengths of spectral signals and predict the oxidation degree of weld bead based on these spectral signals. Finally, through experiments, the prediction validity of the proposed method is verified.